[Enhancement] md2yml pre-commit hook (#732)

* init script

* update scripts and generate new yml

* fix lint: deeplabv3plus.yml

* modify resolution representation

* remove  field

* format crop_size

.dev/md2yml.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python
+
+# This tool is used to update model-index.yml which is required by MIM, and
+# will be automatically called as a pre-commit hook. The updating will be
+# triggered if any change of model information (.md files in configs/) has been
+# detected before a commit.
+
+import glob
+import os
+import os.path as osp
+import sys
+
+import mmcv
+
+MMSEG_ROOT = osp.dirname(osp.dirname((osp.dirname(__file__))))
+
+
+def dump_yaml_and_check_difference(obj, filename):
+    """Dump object to a yaml file, and check if the file content is different
+    from the original.
+
+    Args:
+        obj (any): The python object to be dumped.
+        filename (str): YAML filename to dump the object to.
+    Returns:
+        Bool: If the target YAML file is different from the original.
+    """
+    original = None
+    if osp.isfile(filename):
+        with open(filename, 'r', encoding='utf-8') as f:
+            original = f.read()
+    with open(filename, 'w', encoding='utf-8') as f:
+        mmcv.dump(obj, f, file_format='yaml', sort_keys=False)
+    is_different = True
+    if original is not None:
+        with open(filename, 'r') as f:
+            new = f.read()
+        is_different = (original != new)
+    return is_different
+
+
+def parse_md(md_file):
+    """Parse .md file and convert it to a .yml file which can be used for MIM.
+
+    Args:
+        md_file (str): Path to .md file.
+    Returns:
+        Bool: If the target YAML file is different from the original.
+    """
+    collection_name = osp.dirname(md_file).split('/')[-1]
+    configs = os.listdir(osp.dirname(md_file))
+
+    collection = dict(Name=collection_name, Metadata={'Training Data': []})
+    models = []
+    datasets = []
+
+    with open(md_file, 'r') as md:
+        lines = md.readlines()
+        i = 0
+        current_dataset = ''
+        while i < len(lines):
+            line = lines[i].strip()
+            if len(line) == 0:
+                i += 1
+                continue
+            if line[:3] == '###':
+                datasets.append(line[4:])
+                current_dataset = line[4:]
+                i += 2
+            elif line[0] == '|' and (
+                    i + 1) < len(lines) and lines[i + 1][:3] == '| -':
+                cols = [col.strip() for col in line.split('|')]
+                backbone_id = cols.index('Backbone')
+                crop_size_id = cols.index('Crop Size')
+                lr_schd_id = cols.index('Lr schd')
+                mem_id = cols.index('Mem (GB)')
+                fps_id = cols.index('Inf time (fps)')
+                try:
+                    ss_id = cols.index('mIoU')
+                except ValueError:
+                    ss_id = cols.index('Dice')
+                try:
+                    ms_id = cols.index('mIoU(ms+flip)')
+                except ValueError:
+                    ms_id = False
+                config_id = cols.index('config')
+                download_id = cols.index('download')
+                j = i + 2
+                while j < len(lines) and lines[j][0] == '|':
+                    els = [el.strip() for el in lines[j].split('|')]
+                    config = ''
+                    model_name = ''
+                    weight = ''
+                    for fn in configs:
+                        if fn in els[config_id]:
+                            left = els[download_id].index(
+                                'https://download.openmmlab.com')
+                            right = els[download_id].index('.pth') + 4
+                            weight = els[download_id][left:right]
+                            config = f'configs/{collection_name}/{fn}'
+                            model_name = fn[:-3]
+                    fps = els[fps_id] if els[fps_id] != '-' and els[
+                        fps_id] != '' else -1
+                    mem = els[mem_id] if els[mem_id] != '-' and els[
+                        mem_id] != '' else -1
+                    crop_size = els[crop_size_id].split('x')
+                    assert len(crop_size) == 2
+                    model = {
+                        'Name': model_name,
+                        'In Collection': collection_name,
+                        'Metadata': {
+                            'backbone': els[backbone_id],
+                            'crop size': f'({crop_size[0]},{crop_size[1]})',
+                            'lr schd': int(els[lr_schd_id]),
+                        },
+                        'Results': {
+                            'Task': 'Semantic Segmentation',
+                            'Dataset': current_dataset,
+                            'Metrics': {
+                                'mIoU': float(els[ss_id]),
+                            },
+                        },
+                        'Config': config,
+                        'Weights': weight,
+                    }
+                    if fps != -1:
+                        try:
+                            fps = float(fps)
+                        except Exception:
+                            j += 1
+                            continue
+                        model['Metadata']['inference time (ms/im)'] = [{
+                            'value':
+                            round(1000 / float(fps), 2),
+                            'hardware':
+                            'V100',
+                            'backend':
+                            'PyTorch',
+                            'batch size':
+                            1,
+                            'mode':
+                            'FP32',
+                            'resolution':
+                            f'({crop_size[0]},{crop_size[1]})'
+                        }]
+                    if mem != -1:
+                        model['Metadata']['memory (GB)'] = float(mem)
+                    if ms_id and els[ms_id] != '-' and els[ms_id] != '':
+                        model['Results']['Metrics']['mIoU(ms+flip)'] = float(
+                            els[ms_id])
+                    models.append(model)
+                    j += 1
+                i = j
+            else:
+                i += 1
+    collection['Metadata']['Training Data'] = datasets
+    result = {'Collections': [collection], 'Models': models}
+    yml_file = f'{md_file[:-9]}{collection_name}.yml'
+    return dump_yaml_and_check_difference(result, yml_file)
+
+
+def update_model_index():
+    """Update model-index.yml according to model .md files.
+
+    Returns:
+        Bool: If the updated model-index.yml is different from the original.
+    """
+    configs_dir = osp.join(MMSEG_ROOT, 'configs')
+    yml_files = glob.glob(osp.join(configs_dir, '**', '*.yml'), recursive=True)
+    yml_files.sort()
+
+    model_index = {
+        'Import':
+        [osp.relpath(yml_file, MMSEG_ROOT) for yml_file in yml_files]
+    }
+    model_index_file = osp.join(MMSEG_ROOT, 'model-index.yml')
+    is_different = dump_yaml_and_check_difference(model_index,
+                                                  model_index_file)
+
+    return is_different
+
+
+if __name__ == '__main__':
+    file_list = [fn for fn in sys.argv[1:] if osp.basename(fn) == 'README.md']
+    if not file_list:
+        exit(0)
+    file_modified = False
+    for fn in file_list:
+        file_modified |= parse_md(fn)
+
+    file_modified |= update_model_index()
+
+    exit(1 if file_modified else 0)

.pre-commit-config.yaml

@@ -38,3 +38,12 @@ repos:
     hooks:
       - id: docformatter
         args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: local
+    hooks:
+      - id: update-model-index
+        name: update-model-index
+        description: Collect model information and update model-index.yml
+        entry: .dev/md2yml.py
+        additional_dependencies: [mmcv]
+        language: python
+        files: ^configs/.*\.md$

configs/ann/ann.yml

@@ -0,0 +1,296 @@
+Collections:
+- Name: ann
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: ann_r50-d8_512x1024_40k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 269.54
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.4
+      mIoU(ms+flip): 78.57
+  Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
+- Name: ann_r101-d8_512x1024_40k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 392.16
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.55
+      mIoU(ms+flip): 78.85
+  Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
+- Name: ann_r50-d8_769x769_40k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 588.24
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.89
+      mIoU(ms+flip): 80.46
+  Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
+- Name: ann_r101-d8_769x769_40k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 869.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.32
+      mIoU(ms+flip): 80.94
+  Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
+- Name: ann_r50-d8_512x1024_80k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.34
+      mIoU(ms+flip): 78.65
+  Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
+- Name: ann_r101-d8_512x1024_80k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.14
+      mIoU(ms+flip): 78.81
+  Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
+- Name: ann_r50-d8_769x769_80k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.88
+      mIoU(ms+flip): 80.57
+  Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
+- Name: ann_r101-d8_769x769_80k_cityscapes
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.34
+  Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
+- Name: ann_r50-d8_512x512_80k_ade20k
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.6
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.01
+      mIoU(ms+flip): 42.3
+  Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
+- Name: ann_r101-d8_512x512_80k_ade20k
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.82
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.94
+      mIoU(ms+flip): 44.18
+  Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
+- Name: ann_r50-d8_512x512_160k_ade20k
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.74
+      mIoU(ms+flip): 42.62
+  Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
+- Name: ann_r101-d8_512x512_160k_ade20k
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.94
+      mIoU(ms+flip): 44.06
+  Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
+- Name: ann_r50-d8_512x512_20k_voc12aug
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 47.8
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.86
+      mIoU(ms+flip): 76.13
+  Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
+- Name: ann_r101-d8_512x512_20k_voc12aug
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 71.74
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.47
+      mIoU(ms+flip): 78.7
+  Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
+- Name: ann_r50-d8_512x512_40k_voc12aug
+  In Collection: ann
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.56
+      mIoU(ms+flip): 77.51
+  Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
+- Name: ann_r101-d8_512x512_40k_voc12aug
+  In Collection: ann
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.7
+      mIoU(ms+flip): 78.06
+  Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth

configs/ann/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: ANN
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: ann_r50-d8_512x1024_40k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 269.54
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.40
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
-    Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ann_r101-d8_512x1024_40k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 392.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.55
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
-    Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ann_r50-d8_769x769_40k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 588.24
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.89
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
-    Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: ann_r101-d8_769x769_40k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.32
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
-    Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: ann_r50-d8_512x1024_80k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 269.54
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.34
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
-    Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ann_r101-d8_512x1024_80k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 392.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.14
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
-    Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ann_r50-d8_769x769_80k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 588.24
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.88
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
-    Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: ann_r101-d8_769x769_80k_cityscapes
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
-    Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: ann_r50-d8_512x512_80k_ade20k
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 47.6
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.01
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
-    Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: ann_r101-d8_512x512_80k_ade20k
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 70.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.94
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
-    Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: ann_r50-d8_512x512_160k_ade20k
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 47.6
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.74
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
-    Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: ann_r101-d8_512x512_160k_ade20k
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 70.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.94
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
-    Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: ann_r50-d8_512x512_20k_voc12aug
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 47.8
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 74.86
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
-    Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: ann_r101-d8_512x512_20k_voc12aug
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 71.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
-    Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: ann_r50-d8_512x512_40k_voc12aug
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 47.8
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.56
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
-    Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: ann_r101-d8_512x512_40k_voc12aug
-    In Collection: ANN
-    Metadata:
-      inference time (ms/im):
-        - value: 71.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.70
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
-    Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py

configs/apcnet/apcnet.yml

@@ -0,0 +1,223 @@
+Collections:
+- Name: apcnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+Models:
+- Name: apcnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 280.11
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.02
+      mIoU(ms+flip): 79.26
+  Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
+- Name: apcnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 465.12
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 11.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.08
+      mIoU(ms+flip): 80.34
+  Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
+- Name: apcnet_r50-d8_769x769_40k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 657.89
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.89
+      mIoU(ms+flip): 79.75
+  Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
+- Name: apcnet_r101-d8_769x769_40k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 970.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.96
+      mIoU(ms+flip): 79.24
+  Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
+- Name: apcnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.96
+      mIoU(ms+flip): 79.94
+  Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
+- Name: apcnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.64
+      mIoU(ms+flip): 80.61
+  Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
+- Name: apcnet_r50-d8_769x769_80k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.79
+      mIoU(ms+flip): 80.35
+  Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
+- Name: apcnet_r101-d8_769x769_80k_cityscapes
+  In Collection: apcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.45
+      mIoU(ms+flip): 79.91
+  Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
+- Name: apcnet_r50-d8_512x512_80k_ade20k
+  In Collection: apcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 50.99
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 10.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.2
+      mIoU(ms+flip): 43.3
+  Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
+- Name: apcnet_r101-d8_512x512_80k_ade20k
+  In Collection: apcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 76.34
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 13.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.54
+      mIoU(ms+flip): 46.65
+  Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
+- Name: apcnet_r50-d8_512x512_160k_ade20k
+  In Collection: apcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.4
+      mIoU(ms+flip): 43.94
+  Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
+- Name: apcnet_r101-d8_512x512_160k_ade20k
+  In Collection: apcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.41
+      mIoU(ms+flip): 46.63
+  Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth

configs/apcnet/metafile.yml

@@ -1,234 +0,0 @@
-Collections:
-  - Name: APCNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - ADE20K
-
-Models:
-
-  - Name: apcnet_r50-d8_512x1024_40k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 280.11
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
-    Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: apcnet_r101-d8_512x1024_40k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 465.12
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.08
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
-    Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: apcnet_r50-d8_769x769_40k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 657.89
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.89
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
-    Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: apcnet_r101-d8_769x769_40k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 970.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.96
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
-    Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: apcnet_r50-d8_512x1024_80k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 280.11
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.96
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
-    Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: apcnet_r101-d8_512x1024_80k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 465.12
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.64
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
-    Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: apcnet_r50-d8_769x769_80k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 657.89
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.79
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
-    Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: apcnet_r101-d8_769x769_80k_cityscapes
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 970.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.45
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
-    Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: apcnet_r50-d8_512x512_80k_ade20k
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 50.99
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.20
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
-    Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: apcnet_r101-d8_512x512_80k_ade20k
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 76.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.54
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
-    Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: apcnet_r50-d8_512x512_160k_ade20k
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 50.99
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.40
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
-    Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: apcnet_r101-d8_512x512_160k_ade20k
-    In Collection: APCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 76.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
-    Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py

configs/ccnet/ccnet.yml

@@ -0,0 +1,296 @@
+Collections:
+- Name: ccnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: ccnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 301.2
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.76
+      mIoU(ms+flip): 78.87
+  Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
+- Name: ccnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 432.9
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.35
+      mIoU(ms+flip): 78.19
+  Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
+- Name: ccnet_r50-d8_769x769_40k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 699.3
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.46
+      mIoU(ms+flip): 79.93
+  Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
+- Name: ccnet_r101-d8_769x769_40k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 990.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.94
+      mIoU(ms+flip): 78.62
+  Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
+- Name: ccnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.03
+      mIoU(ms+flip): 80.16
+  Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
+- Name: ccnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.87
+      mIoU(ms+flip): 79.9
+  Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
+- Name: ccnet_r50-d8_769x769_80k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.29
+      mIoU(ms+flip): 81.08
+  Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
+- Name: ccnet_r101-d8_769x769_80k_cityscapes
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.45
+      mIoU(ms+flip): 80.66
+  Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
+- Name: ccnet_r50-d8_512x512_80k_ade20k
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.78
+      mIoU(ms+flip): 42.98
+  Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
+- Name: ccnet_r101-d8_512x512_80k_ade20k
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.97
+      mIoU(ms+flip): 45.13
+  Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
+- Name: ccnet_r50-d8_512x512_160k_ade20k
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.08
+      mIoU(ms+flip): 43.13
+  Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
+- Name: ccnet_r101-d8_512x512_160k_ade20k
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.71
+      mIoU(ms+flip): 45.04
+  Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
+- Name: ccnet_r50-d8_512x512_20k_voc12aug
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 48.9
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.17
+      mIoU(ms+flip): 77.51
+  Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
+- Name: ccnet_r101-d8_512x512_20k_voc12aug
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 73.31
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.27
+      mIoU(ms+flip): 79.02
+  Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
+- Name: ccnet_r50-d8_512x512_40k_voc12aug
+  In Collection: ccnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.96
+      mIoU(ms+flip): 77.04
+  Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
+- Name: ccnet_r101-d8_512x512_40k_voc12aug
+  In Collection: ccnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.87
+      mIoU(ms+flip): 78.9
+  Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth

configs/ccnet/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: CCNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: ccnet_r50-d8_512x1024_40k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 301.2
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.76
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
-    Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ccnet_r101-d8_512x1024_40k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 432.9
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.35
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
-    Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ccnet_r50-d8_769x769_40k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 699.3
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.46
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
-    Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: ccnet_r101-d8_769x769_40k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 990.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.94
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
-    Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: ccnet_r50-d8_512x1024_80k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 301.2
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.03
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
-    Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ccnet_r101-d8_512x1024_80k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 432.9
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
-    Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ccnet_r50-d8_769x769_80k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 699.3
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.29
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
-    Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: ccnet_r101-d8_769x769_80k_cityscapes
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 990.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.45
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
-    Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: ccnet_r50-d8_512x512_80k_ade20k
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.78
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
-    Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: ccnet_r101-d8_512x512_80k_ade20k
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 70.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.97
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
-    Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: ccnet_r50-d8_512x512_160k_ade20k
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.08
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
-    Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: ccnet_r101-d8_512x512_160k_ade20k
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 70.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.71
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
-    Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: ccnet_r50-d8_512x512_20k_voc12aug
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 48.9
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.17
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
-    Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: ccnet_r101-d8_512x512_20k_voc12aug
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 73.31
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.27
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
-    Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: ccnet_r50-d8_512x512_40k_voc12aug
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 48.9
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 75.96
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
-    Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: ccnet_r101-d8_512x512_40k_voc12aug
-    In Collection: CCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 73.31
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
-    Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py

configs/cgnet/cgnet.yml

@@ -0,0 +1,50 @@
+Collections:
+- Name: cgnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+Models:
+- Name: cgnet_680x680_60k_cityscapes
+  In Collection: cgnet
+  Metadata:
+    backbone: M3N21
+    crop size: (680,680)
+    lr schd: 60000
+    inference time (ms/im):
+    - value: 32.78
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (680,680)
+    memory (GB): 7.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 65.63
+      mIoU(ms+flip): 68.04
+  Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
+- Name: cgnet_512x1024_60k_cityscapes
+  In Collection: cgnet
+  Metadata:
+    backbone: M3N21
+    crop size: (512,1024)
+    lr schd: 60000
+    inference time (ms/im):
+    - value: 32.11
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 68.27
+      mIoU(ms+flip): 70.33
+  Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth

configs/cgnet/metafile.yml

@@ -1,43 +0,0 @@
-Collections:
-  - Name: CGNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-
-Models:
-
-  - Name: cgnet_680x680_60k_cityscapes
-    In Collection: CGNet
-    Metadata:
-      inference time (ms/im):
-        - value: 32.78
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 65.63
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
-    Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
-
-
-
-  - Name: cgnet_512x1024_60k_cityscapes
-    In Collection: CGNet
-    Metadata:
-      inference time (ms/im):
-        - value: 32.11
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 68.27
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
-    Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py

configs/danet/danet.yml

@@ -0,0 +1,292 @@
+Collections:
+- Name: danet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: danet_r50-d8_512x1024_40k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 375.94
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.74
+  Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
+- Name: danet_r101-d8_512x1024_40k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 502.51
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 10.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.52
+  Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
+- Name: danet_r50-d8_769x769_40k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 641.03
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.88
+      mIoU(ms+flip): 80.62
+  Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
+- Name: danet_r101-d8_769x769_40k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 934.58
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.88
+      mIoU(ms+flip): 81.47
+  Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
+- Name: danet_r50-d8_512x1024_80k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.34
+  Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
+- Name: danet_r101-d8_512x1024_80k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.41
+  Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
+- Name: danet_r50-d8_769x769_80k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.27
+      mIoU(ms+flip): 80.96
+  Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
+- Name: danet_r101-d8_769x769_80k_cityscapes
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.47
+      mIoU(ms+flip): 82.02
+  Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
+- Name: danet_r50-d8_512x512_80k_ade20k
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.17
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 11.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.66
+      mIoU(ms+flip): 42.9
+  Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
+- Name: danet_r101-d8_512x512_80k_ade20k
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.52
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 15.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.64
+      mIoU(ms+flip): 45.19
+  Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
+- Name: danet_r50-d8_512x512_160k_ade20k
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.45
+      mIoU(ms+flip): 43.25
+  Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
+- Name: danet_r101-d8_512x512_160k_ade20k
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.17
+      mIoU(ms+flip): 45.02
+  Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
+- Name: danet_r50-d8_512x512_20k_voc12aug
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 47.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.45
+      mIoU(ms+flip): 75.69
+  Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
+- Name: danet_r101-d8_512x512_20k_voc12aug
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 72.67
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.02
+      mIoU(ms+flip): 77.23
+  Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
+- Name: danet_r50-d8_512x512_40k_voc12aug
+  In Collection: danet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.37
+      mIoU(ms+flip): 77.29
+  Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
+- Name: danet_r101-d8_512x512_40k_voc12aug
+  In Collection: danet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.51
+      mIoU(ms+flip): 77.32
+  Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth

configs/danet/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: DANet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: danet_r50-d8_512x1024_40k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 375.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.74
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
-    Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: danet_r101-d8_512x1024_40k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 502.51
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.52
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
-    Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: danet_r50-d8_769x769_40k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 641.03
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.88
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
-    Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: danet_r101-d8_769x769_40k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 934.58
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.88
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
-    Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: danet_r50-d8_512x1024_80k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 375.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.34
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
-    Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: danet_r101-d8_512x1024_80k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 502.51
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
-    Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: danet_r50-d8_769x769_80k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 641.03
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.27
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
-    Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: danet_r101-d8_769x769_80k_cityscapes
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 934.58
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
-    Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: danet_r50-d8_512x512_80k_ade20k
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.17
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.66
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
-    Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: danet_r101-d8_512x512_80k_ade20k
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 70.52
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.64
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
-    Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: danet_r50-d8_512x512_160k_ade20k
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.17
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.45
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
-    Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: danet_r101-d8_512x512_160k_ade20k
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 70.52
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 44.17
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
-    Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: danet_r50-d8_512x512_20k_voc12aug
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 74.45
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
-    Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: danet_r101-d8_512x512_20k_voc12aug
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 72.67
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
-    Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: danet_r50-d8_512x512_40k_voc12aug
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.37
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
-    Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: danet_r101-d8_512x512_40k_voc12aug
-    In Collection: DANet
-    Metadata:
-      inference time (ms/im):
-        - value: 72.67
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.51
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
-    Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py

configs/deeplabv3/deeplabv3.yml

@@ -0,0 +1,552 @@
+Collections:
+- Name: deeplabv3
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+Models:
+- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 389.11
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.09
+      mIoU(ms+flip): 80.45
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
+- Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 520.83
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.12
+      mIoU(ms+flip): 79.61
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
+- Name: deeplabv3_r50-d8_769x769_40k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 900.9
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.58
+      mIoU(ms+flip): 79.89
+  Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
+- Name: deeplabv3_r101-d8_769x769_40k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 1204.82
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.27
+      mIoU(ms+flip): 80.11
+  Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
+- Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 72.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.7
+      mIoU(ms+flip): 78.27
+  Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
+- Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.32
+      mIoU(ms+flip): 80.57
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
+- Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.2
+      mIoU(ms+flip): 81.21
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
+- Name: deeplabv3_r18-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-18-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 180.18
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 1.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.6
+      mIoU(ms+flip): 78.26
+  Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
+- Name: deeplabv3_r50-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.89
+      mIoU(ms+flip): 81.06
+  Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
+- Name: deeplabv3_r101-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.67
+      mIoU(ms+flip): 80.81
+  Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
+- Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D16-MG124
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.36
+      mIoU(ms+flip): 79.84
+  Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
+- Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 71.79
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.26
+      mIoU(ms+flip): 77.88
+  Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
+- Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 364.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.63
+      mIoU(ms+flip): 80.98
+  Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
+- Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 552.49
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.01
+      mIoU(ms+flip): 81.21
+  Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
+- Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 172.71
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 1.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.63
+      mIoU(ms+flip): 77.51
+  Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
+- Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 862.07
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.27
+  Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
+- Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 1219.51
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.73
+  Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
+- Name: deeplabv3_r50-d8_512x512_80k_ade20k
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 67.75
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.42
+      mIoU(ms+flip): 43.28
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
+- Name: deeplabv3_r101-d8_512x512_80k_ade20k
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 98.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.08
+      mIoU(ms+flip): 45.19
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
+- Name: deeplabv3_r50-d8_512x512_160k_ade20k
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.66
+      mIoU(ms+flip): 44.09
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
+- Name: deeplabv3_r101-d8_512x512_160k_ade20k
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.0
+      mIoU(ms+flip): 46.66
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
+- Name: deeplabv3_r50-d8_512x512_20k_voc12aug
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 72.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.17
+      mIoU(ms+flip): 77.42
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
+- Name: deeplabv3_r101-d8_512x512_20k_voc12aug
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 101.94
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.7
+      mIoU(ms+flip): 79.95
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
+- Name: deeplabv3_r50-d8_512x512_40k_voc12aug
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.68
+      mIoU(ms+flip): 78.78
+  Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
+- Name: deeplabv3_r101-d8_512x512_40k_voc12aug
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.92
+      mIoU(ms+flip): 79.18
+  Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
+- Name: deeplabv3_r101-d8_480x480_40k_pascal_context
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 141.04
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.55
+      mIoU(ms+flip): 47.81
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
+- Name: deeplabv3_r101-d8_480x480_80k_pascal_context
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.42
+      mIoU(ms+flip): 47.53
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
+- Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.61
+      mIoU(ms+flip): 54.28
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
+- Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
+  In Collection: deeplabv3
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.46
+      mIoU(ms+flip): 54.09
+  Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth

configs/deeplabv3/metafile.yml

@@ -1,578 +0,0 @@
-Collections:
-  - Name: DeepLabV3
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal Context
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 389.11
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.09
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 520.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.12
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r50-d8_769x769_40k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 900.9
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.58
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d8_769x769_40k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 1204.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.27
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 72.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.70
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
-    Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 389.11
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.32
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 520.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.20
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r18-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 180.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.60
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
-    Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r50-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 900.9
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.89
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 1204.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.67
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 143.68
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.71
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 143.68
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.36
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 71.79
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.26
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
-    Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 364.96
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.63
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
-    Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 552.49
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.01
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
-    Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 172.71
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.63
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
-    Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 862.07
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
-    Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 1219.51
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
-    Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_r50-d8_512x512_80k_ade20k
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 67.75
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.42
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x512_80k_ade20k
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 98.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 44.08
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: deeplabv3_r50-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 67.75
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.66
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 98.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.00
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3_r50-d8_512x512_20k_voc12aug
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 72.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.17
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x512_20k_voc12aug
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 101.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 78.70
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: deeplabv3_r50-d8_512x512_40k_voc12aug
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 72.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.68
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
-    Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x512_40k_voc12aug
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 101.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.92
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: deeplabv3_r101-d8_480x480_40k_pascal_context
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 141.04
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 46.55
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: deeplabv3_r101-d8_480x480_80k_pascal_context
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 141.04
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 46.42
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
-
-
-
-  - Name: deeplabv3_r101-d8_480x480_40k_pascal_context
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 52.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 52.46
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py

configs/deeplabv3plus/deeplabv3plus.yml

@@ -0,0 +1,574 @@
+Collections:
+- Name: deeplabv3plus
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - ' Pascal VOC 2012 + Aug'
+    - ' Pascal Context'
+    - ' Pascal Context 59'
+Models:
+- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 253.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.61
+      mIoU(ms+flip): 81.01
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
+- Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 384.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 11.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.21
+      mIoU(ms+flip): 81.82
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
+- Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 581.4
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.97
+      mIoU(ms+flip): 80.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
+- Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 869.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.46
+      mIoU(ms+flip): 80.5
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
+- Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.08
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 2.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.89
+      mIoU(ms+flip): 78.76
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
+- Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.09
+      mIoU(ms+flip): 81.13
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
+- Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.97
+      mIoU(ms+flip): 82.03
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
+- Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-18-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 174.22
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 2.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.26
+      mIoU(ms+flip): 77.91
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
+- Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.83
+      mIoU(ms+flip): 81.48
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
+- Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.98
+      mIoU(ms+flip): 82.18
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth
+- Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D16-MG124
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 133.69
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.09
+      mIoU(ms+flip): 80.36
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
+- Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D16-MG124
+    crop size: (512,1024)
+    lr schd: 80000
+    memory (GB): 9.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.9
+      mIoU(ms+flip): 81.33
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
+- Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 66.89
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 2.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.87
+      mIoU(ms+flip): 77.52
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
+- Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 253.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.28
+      mIoU(ms+flip): 81.44
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
+- Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 384.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 10.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.16
+      mIoU(ms+flip): 81.41
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
+- Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 167.79
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 2.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.36
+      mIoU(ms+flip): 78.24
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
+- Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 581.4
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.56
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
+- Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 909.09
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.88
+      mIoU(ms+flip): 81.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
+- Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.6
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 10.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.72
+      mIoU(ms+flip): 43.75
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
+- Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 14.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.6
+      mIoU(ms+flip): 46.06
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
+- Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.95
+      mIoU(ms+flip): 44.93
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
+- Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.47
+      mIoU(ms+flip): 46.35
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
+- Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 47.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal VOC 2012 + Aug'
+    Metrics:
+      mIoU: 75.93
+      mIoU(ms+flip): 77.5
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
+- Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 72.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 11.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal VOC 2012 + Aug'
+    Metrics:
+      mIoU: 77.22
+      mIoU(ms+flip): 78.59
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
+- Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal VOC 2012 + Aug'
+    Metrics:
+      mIoU: 76.81
+      mIoU(ms+flip): 77.57
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
+- Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal VOC 2012 + Aug'
+    Metrics:
+      mIoU: 78.62
+      mIoU(ms+flip): 79.53
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
+- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 110.01
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal Context'
+    Metrics:
+      mIoU: 47.3
+      mIoU(ms+flip): 48.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
+- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal Context'
+    Metrics:
+      mIoU: 47.23
+      mIoU(ms+flip): 48.26
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
+- Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal Context 59'
+    Metrics:
+      mIoU: 52.86
+      mIoU(ms+flip): 54.54
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
+- Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
+  In Collection: deeplabv3plus
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' Pascal Context 59'
+    Metrics:
+      mIoU: 53.2
+      mIoU(ms+flip): 54.67
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth

configs/deeplabv3plus/metafile.yml

@@ -1,578 +0,0 @@
-Collections:
-  - Name: DeepLabV3+
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal Context
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 253.81
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 384.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.21
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 581.4
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.97
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.46
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 70.08
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.89
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 253.81
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.09
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 384.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.97
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 174.22
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.26
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 581.4
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.83
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.98
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 133.69
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.09
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 133.69
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.90
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 66.89
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 253.81
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.28
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 384.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.16
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 167.79
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.36
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 581.4
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 909.09
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.88
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 47.6
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.72
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 70.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 44.60
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 47.6
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.95
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 70.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 47.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 75.93
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 72.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.22
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 47.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.81
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 72.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 78.62
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 110.01
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 47.30
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 110.01
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 47.23
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 52.86
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 53.2
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py

configs/dmnet/dmnet.yml

@@ -0,0 +1,223 @@
+Collections:
+- Name: dmnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+Models:
+- Name: dmnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 273.22
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.78
+      mIoU(ms+flip): 79.14
+  Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
+- Name: dmnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 393.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 10.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.37
+      mIoU(ms+flip): 79.72
+  Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
+- Name: dmnet_r50-d8_769x769_40k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 636.94
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 7.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.49
+      mIoU(ms+flip): 80.27
+  Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
+- Name: dmnet_r101-d8_769x769_40k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 990.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.62
+      mIoU(ms+flip): 78.94
+  Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
+- Name: dmnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.07
+      mIoU(ms+flip): 80.22
+  Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
+- Name: dmnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.64
+      mIoU(ms+flip): 80.67
+  Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
+- Name: dmnet_r50-d8_769x769_80k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.22
+      mIoU(ms+flip): 80.55
+  Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
+- Name: dmnet_r101-d8_769x769_80k_cityscapes
+  In Collection: dmnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.19
+      mIoU(ms+flip): 80.65
+  Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
+- Name: dmnet_r50-d8_512x512_80k_ade20k
+  In Collection: dmnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.73
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.37
+      mIoU(ms+flip): 43.62
+  Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
+- Name: dmnet_r101-d8_512x512_80k_ade20k
+  In Collection: dmnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 72.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 13.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.34
+      mIoU(ms+flip): 46.13
+  Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
+- Name: dmnet_r50-d8_512x512_160k_ade20k
+  In Collection: dmnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.15
+      mIoU(ms+flip): 44.17
+  Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
+- Name: dmnet_r101-d8_512x512_160k_ade20k
+  In Collection: dmnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.42
+      mIoU(ms+flip): 46.76
+  Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth

configs/dmnet/metafile.yml

@@ -1,234 +0,0 @@
-Collections:
-  - Name: DMNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - ADE20K
-
-Models:
-
-  - Name: dmnet_r50-d8_512x1024_40k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 273.22
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.78
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
-    Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: dmnet_r101-d8_512x1024_40k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 393.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.37
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
-    Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: dmnet_r50-d8_769x769_40k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 636.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.49
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
-    Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: dmnet_r101-d8_769x769_40k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 990.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.62
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
-    Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: dmnet_r50-d8_512x1024_80k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 273.22
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.07
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
-    Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: dmnet_r101-d8_512x1024_80k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 393.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.64
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
-    Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: dmnet_r50-d8_769x769_80k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 636.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.22
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
-    Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: dmnet_r101-d8_769x769_80k_cityscapes
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 990.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.19
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
-    Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: dmnet_r50-d8_512x512_80k_ade20k
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.73
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.37
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
-    Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: dmnet_r101-d8_512x512_80k_ade20k
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 72.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.34
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
-    Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: dmnet_r50-d8_512x512_160k_ade20k
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 47.73
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.15
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
-    Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: dmnet_r101-d8_512x512_160k_ade20k
-    In Collection: DMNet
-    Metadata:
-      inference time (ms/im):
-        - value: 72.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.42
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
-    Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py

configs/dnlnet/dnlnet.yml

@@ -0,0 +1,219 @@
+Collections:
+- Name: dnlnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+Models:
+- Name: dnl_r50-d8_512x1024_40k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 390.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.61
+  Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
+- Name: dnl_r101-d8_512x1024_40k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 510.2
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 10.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.31
+  Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
+- Name: dnl_r50-d8_769x769_40k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 666.67
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.44
+      mIoU(ms+flip): 80.27
+  Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
+- Name: dnl_r101-d8_769x769_40k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 980.39
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.39
+      mIoU(ms+flip): 77.77
+  Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
+- Name: dnl_r50-d8_512x1024_80k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.33
+  Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
+- Name: dnl_r101-d8_512x1024_80k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.41
+  Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
+- Name: dnl_r50-d8_769x769_80k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.36
+      mIoU(ms+flip): 80.7
+  Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
+- Name: dnl_r101-d8_769x769_80k_cityscapes
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.68
+  Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
+- Name: dnl_r50-d8_512x512_80k_ade20k
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 48.4
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.76
+      mIoU(ms+flip): 42.99
+  Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
+- Name: dnl_r101-d8_512x512_80k_ade20k
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 79.74
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.76
+      mIoU(ms+flip): 44.91
+  Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
+- Name: dnl_r50-d8_512x512_160k_ade20k
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.87
+      mIoU(ms+flip): 43.01
+  Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
+- Name: dnl_r101-d8_512x512_160k_ade20k
+  In Collection: dnlnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.25
+      mIoU(ms+flip): 45.78
+  Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth

configs/dnlnet/metafile.yml

@@ -1,234 +0,0 @@
-Collections:
-  - Name: dnl
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - ADE20K
-
-Models:
-
-  - Name: dnl_r50-d8_512x1024_40k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 390.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
-    Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: dnl_r101-d8_512x1024_40k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 510.2
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.31
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
-    Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: dnl_r50-d8_769x769_40k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 666.67
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.44
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
-    Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: dnl_r101-d8_769x769_40k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 980.39
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.39
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
-    Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: dnl_r50-d8_512x1024_80k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 390.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.33
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
-    Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: dnl_r101-d8_512x1024_80k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 510.2
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
-    Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: dnl_r50-d8_769x769_80k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 666.67
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.36
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
-    Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: dnl_r101-d8_769x769_80k_cityscapes
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 980.39
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
-    Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: dnl_r50-d8_512x512_80k_ade20k
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 48.4
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.76
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
-    Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: dnl_r101-d8_512x512_80k_ade20k
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 79.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.76
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
-    Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: dnl_r50-d8_512x512_160k_ade20k
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 48.4
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
-    Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: dnl_r101-d8_512x512_160k_ade20k
-    In Collection: dnl
-    Metadata:
-      inference time (ms/im):
-        - value: 79.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 44.25
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
-    Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py

configs/emanet/emanet.yml

@@ -0,0 +1,94 @@
+Collections:
+- Name: emanet
+  Metadata:
+    Training Data:
+    - Cityscapes
+Models:
+- Name: emanet_r50-d8_512x1024_80k_cityscapes
+  In Collection: emanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 218.34
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.59
+      mIoU(ms+flip): 79.44
+  Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
+- Name: emanet_r101-d8_512x1024_80k_cityscapes
+  In Collection: emanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 348.43
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.1
+      mIoU(ms+flip): 81.21
+  Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
+- Name: emanet_r50-d8_769x769_80k_cityscapes
+  In Collection: emanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 507.61
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.33
+      mIoU(ms+flip): 80.49
+  Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
+- Name: emanet_r101-d8_769x769_80k_cityscapes
+  In Collection: emanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 819.67
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.62
+      mIoU(ms+flip): 81.0
+  Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth

configs/emanet/metafile.yml

@@ -1,81 +0,0 @@
-Collections:
-  - Name: EMANet
-    Metadata:
-      Training Data:
-        - Cityscapes
-
-Models:
-
-  - Name: emanet_r50-d8_512x1024_80k_cityscapes
-    In Collection: EMANet
-    Metadata:
-      inference time (ms/im):
-        - value: 218.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.59
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
-    Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: emanet_r101-d8_512x1024_80k_cityscapes
-    In Collection: EMANet
-    Metadata:
-      inference time (ms/im):
-        - value: 348.43
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
-    Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: emanet_r50-d8_769x769_80k_cityscapes
-    In Collection: EMANet
-    Metadata:
-      inference time (ms/im):
-        - value: 507.61
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.33
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
-    Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: emanet_r101-d8_769x769_80k_cityscapes
-    In Collection: EMANet
-    Metadata:
-      inference time (ms/im):
-        - value: 819.67
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.62
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
-    Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py

configs/encnet/encnet.yml

@@ -0,0 +1,223 @@
+Collections:
+- Name: encnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+Models:
+- Name: encnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 218.34
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.67
+      mIoU(ms+flip): 77.08
+  Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
+- Name: encnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 375.94
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 12.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.81
+      mIoU(ms+flip): 77.21
+  Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
+- Name: encnet_r50-d8_769x769_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 549.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 9.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 77.85
+  Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
+- Name: encnet_r101-d8_769x769_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 793.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 13.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.25
+      mIoU(ms+flip): 76.25
+  Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
+- Name: encnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.94
+      mIoU(ms+flip): 79.13
+  Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
+- Name: encnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.55
+      mIoU(ms+flip): 79.47
+  Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
+- Name: encnet_r50-d8_769x769_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.44
+      mIoU(ms+flip): 78.72
+  Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
+- Name: encnet_r101-d8_769x769_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.1
+      mIoU(ms+flip): 76.97
+  Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
+- Name: encnet_r50-d8_512x512_80k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 43.84
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 10.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.53
+      mIoU(ms+flip): 41.17
+  Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
+- Name: encnet_r101-d8_512x512_80k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 67.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 13.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.11
+      mIoU(ms+flip): 43.61
+  Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
+- Name: encnet_r50-d8_512x512_160k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.1
+      mIoU(ms+flip): 41.71
+  Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
+- Name: encnet_r101-d8_512x512_160k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.61
+      mIoU(ms+flip): 44.01
+  Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth

configs/encnet/metafile.yml

@@ -1,235 +0,0 @@
-Collections:
-  - Name: encnet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: encnet_r50-d8_512x1024_40k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 218.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.67
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
-    Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: encnet_r101-d8_512x1024_40k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 375.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.81
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
-    Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: encnet_r50-d8_769x769_40k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 549.45
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.24
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
-    Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: encnet_r101-d8_769x769_40k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 793.65
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 74.25
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
-    Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: encnet_r50-d8_512x1024_80k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 218.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.94
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
-    Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: encnet_r101-d8_512x1024_80k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 375.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.55
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
-    Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: encnet_r50-d8_769x769_80k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 549.45
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.44
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
-    Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: encnet_r101-d8_769x769_80k_cityscapes
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 793.65
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
-    Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: encnet_r50-d8_512x512_80k_ade20k
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 43.84
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 39.53
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
-    Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: encnet_r101-d8_512x512_80k_ade20k
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 67.25
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.11
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
-    Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: encnet_r50-d8_512x512_160k_ade20k
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 43.84
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 40.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
-    Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: encnet_r101-d8_512x512_160k_ade20k
-    In Collection: encnet
-    Metadata:
-      inference time (ms/im):
-        - value: 67.25
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
-    Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py

configs/fastscnn/fastscnn.yml

@@ -0,0 +1,28 @@
+Collections:
+- Name: fastscnn
+  Metadata:
+    Training Data:
+    - Cityscapes
+Models:
+- Name: ''
+  In Collection: fastscnn
+  Metadata:
+    backbone: Fast-SCNN
+    crop size: (512,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 17.71
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.96
+      mIoU(ms+flip): 72.65
+  Config: ''
+  Weights: ''

configs/fastscnn/metafile.yml

@@ -1,24 +0,0 @@
-Collections:
-  - Name: Fast-SCNN
-    Metadata:
-      Training Data:
-        - Cityscapes
-
-Models:
-
-  - Name: fast_scnn_4x8_80k_lr0.12_cityscapes
-    In Collection: Fast-SCNN
-    Metadata:
-      inference time (ms/im):
-        - value: 15.72
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 69.06
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth
-    Config: configs/fast-scnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py

configs/fcn/README.md

@@ -47,10 +47,10 @@
 | FCN-D6 | R-101-D16  | 512x1024  |   80000 | -        | 8.26           | 78.46 |         80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json)     |
 | FCN-D6 | R-101-D16  | 769x769   |   40000 | 5.0      | 3.12           | 77.28 |         78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json)         |
 | FCN-D6 | R-101-D16  | 769x769   |   80000 | -        | 3.21           | 78.06 |         79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json)         |
-| FCN-D6 | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
-| FCN-D6 | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
-| FCN-D6 | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
-| FCN-D6 | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
+| FCN-D6 | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
+| FCN-D6 | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
+| FCN-D6 | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
+| FCN-D6 | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
 
 ### ADE20K
 

configs/fcn/fcn.yml

@@ -0,0 +1,797 @@
+Collections:
+- Name: fcn
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+Models:
+- Name: fcn_r50-d8_512x1024_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 239.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.25
+      mIoU(ms+flip): 73.36
+  Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
+- Name: fcn_r101-d8_512x1024_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 375.94
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.45
+      mIoU(ms+flip): 76.58
+  Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
+- Name: fcn_r50-d8_769x769_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 555.56
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.47
+      mIoU(ms+flip): 72.54
+  Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
+- Name: fcn_r101-d8_769x769_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 840.34
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.93
+      mIoU(ms+flip): 75.14
+  Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
+- Name: fcn_r18-d8_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 68.26
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.11
+      mIoU(ms+flip): 72.91
+  Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
+- Name: fcn_r50-d8_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.61
+      mIoU(ms+flip): 74.24
+  Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
+- Name: fcn_r101-d8_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.13
+      mIoU(ms+flip): 75.94
+  Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
+- Name: fcn_r18-d8_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-18-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 156.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 1.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.8
+      mIoU(ms+flip): 73.16
+  Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
+- Name: fcn_r50-d8_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.64
+      mIoU(ms+flip): 73.32
+  Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
+- Name: fcn_r101-d8_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.52
+      mIoU(ms+flip): 76.61
+  Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
+- Name: fcn_r18b-d8_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 59.74
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.24
+      mIoU(ms+flip): 72.77
+  Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
+- Name: fcn_r50b-d8_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 238.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.65
+      mIoU(ms+flip): 77.59
+  Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
+- Name: fcn_r101b-d8_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 366.3
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.37
+      mIoU(ms+flip): 78.77
+  Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
+- Name: fcn_r18b-d8_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 149.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 1.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 69.66
+      mIoU(ms+flip): 72.07
+  Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
+- Name: fcn_r50b-d8_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 549.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.83
+      mIoU(ms+flip): 76.6
+  Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
+- Name: fcn_r101b-d8_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 869.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.02
+      mIoU(ms+flip): 78.67
+  Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
+- Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D16
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 97.85
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.06
+      mIoU(ms+flip): 78.85
+  Config: configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
+- Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D16
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 96.62
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.27
+      mIoU(ms+flip): 78.88
+  Config: configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
+- Name: fcn_d6_r50-d16_769x769_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D16
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 239.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 3.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.82
+      mIoU(ms+flip): 78.22
+  Config: configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth
+- Name: fcn_d6_r50-d16_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D16
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 240.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.04
+      mIoU(ms+flip): 78.4
+  Config: configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth
+- Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D16
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 124.38
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 4.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.36
+      mIoU(ms+flip): 79.18
+  Config: configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth
+- Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D16
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 121.07
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.46
+      mIoU(ms+flip): 80.42
+  Config: configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth
+- Name: fcn_d6_r101-d16_769x769_40k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D16
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 320.51
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 5.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.28
+      mIoU(ms+flip): 78.95
+  Config: configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth
+- Name: fcn_d6_r101-d16_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D16
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 311.53
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.06
+      mIoU(ms+flip): 79.58
+  Config: configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth
+- Name: fcn_d6_r50b-d16_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50b-D16
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 98.43
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.99
+      mIoU(ms+flip): 79.03
+  Config: configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth
+- Name: fcn_d6_r50b-d16_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-50b-D16
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 239.81
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 3.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.86
+      mIoU(ms+flip): 78.52
+  Config: configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth
+- Name: fcn_d6_r101b-d16_512x1024_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101b-D16
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 118.2
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 4.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.72
+      mIoU(ms+flip): 79.53
+  Config: configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth
+- Name: fcn_d6_r101b-d16_769x769_80k_cityscapes
+  In Collection: fcn
+  Metadata:
+    backbone: R-101b-D16
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 301.2
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 4.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.34
+      mIoU(ms+flip): 78.91
+  Config: configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth
+- Name: fcn_r50-d8_512x512_80k_ade20k
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 42.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 35.94
+      mIoU(ms+flip): 37.94
+  Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
+- Name: fcn_r101-d8_512x512_80k_ade20k
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 67.66
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.61
+      mIoU(ms+flip): 40.83
+  Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
+- Name: fcn_r50-d8_512x512_160k_ade20k
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.1
+      mIoU(ms+flip): 38.08
+  Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
+- Name: fcn_r101-d8_512x512_160k_ade20k
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.91
+      mIoU(ms+flip): 41.4
+  Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
+- Name: fcn_r50-d8_512x512_20k_voc12aug
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 42.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 67.08
+      mIoU(ms+flip): 69.94
+  Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
+- Name: fcn_r101-d8_512x512_20k_voc12aug
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 67.52
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 71.16
+      mIoU(ms+flip): 73.57
+  Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
+- Name: fcn_r50-d8_512x512_40k_voc12aug
+  In Collection: fcn
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 66.97
+      mIoU(ms+flip): 69.04
+  Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
+- Name: fcn_r101-d8_512x512_40k_voc12aug
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 69.91
+      mIoU(ms+flip): 72.38
+  Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
+- Name: fcn_r101-d8_480x480_40k_pascal_context
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 100.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 44.43
+      mIoU(ms+flip): 45.63
+  Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth
+- Name: fcn_r101-d8_480x480_80k_pascal_context
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 44.13
+      mIoU(ms+flip): 45.26
+  Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth
+- Name: fcn_r101-d8_480x480_40k_pascal_context_59
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 48.42
+      mIoU(ms+flip): 50.4
+  Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
+- Name: fcn_r101-d8_480x480_80k_pascal_context_59
+  In Collection: fcn
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 49.35
+      mIoU(ms+flip): 51.38
+  Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth

configs/fcn/metafile.yml

@@ -1,699 +0,0 @@
-Collections:
-  - Name: FCN
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal Context
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-  - Name: FCN-D6
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal Context
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: fcn_r50-d8_512x1024_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 239.81
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 72.25
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
-    Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_r101-d8_512x1024_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 375.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.45
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
-    Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_r50-d8_769x769_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 555.56
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 71.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
-    Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: fcn_r101-d8_769x769_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 840.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 73.93
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
-    Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: fcn_r18-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 68.26
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 71.11
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
-    Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_r50-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 239.81
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 73.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
-    Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_r101-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 375.94
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.13
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
-    Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_r18-d8_769x769_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 156.25
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 70.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
-    Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_r50-d8_769x769_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 555.56
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 72.64
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
-    Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_r101-d8_769x769_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 840.34
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.52
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
-    Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_r18b-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 59.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 70.24
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
-    Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_r50b-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 238.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.65
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
-    Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_r101b-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 366.3
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.37
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
-    Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_r18b-d8_769x769_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 149.25
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 69.66
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
-    Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_r50b-d8_769x769_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 549.45
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 73.83
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
-    Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_r101b-d8_769x769_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
-    Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 97.85
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.06
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
-    Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 96.62
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.27
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
-    Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r50-d16_769x769_40k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 239.81
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.82
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth
-    Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r50-d16_769x769_80k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 240.96
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.04
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth
-    Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 124.38
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.36
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth
-    Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 121.07
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.46
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth
-    Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r101-d16_769x769_40k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 320.51
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.28
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth
-    Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes.py
-
-
-
-  - Name: fcn_d6_r101-d16_769x769_80k_cityscapes
-    In Collection: FCN-D6
-    Metadata:
-      inference time (ms/im):
-        - value: 311.53
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.06
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth
-    Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes.py
-
-
-
-  - Name: fcn_r50-d8_512x512_80k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 35.94
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
-    Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: fcn_r101-d8_512x512_80k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 67.66
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 39.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
-    Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: fcn_r50-d8_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 36.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
-    Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: fcn_r101-d8_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 67.66
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 39.91
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
-    Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: fcn_r50-d8_512x512_20k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.96
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 67.08
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
-    Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: fcn_r101-d8_512x512_20k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 67.52
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 71.16
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
-    Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: fcn_r50-d8_512x512_40k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.96
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 66.97
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
-    Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: fcn_r101-d8_512x512_40k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 67.52
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 69.91
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
-    Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: fcn_r101-d8_480x480_40k_pascal_context
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 100.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 44.43
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth
-    Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: fcn_r101-d8_480x480_80k_pascal_context
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 100.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 44.13
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth
-    Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
-
-
-
-  - Name: fcn_r101-d8_480x480_40k_pascal_context_59
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 48.42
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
-    Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
-
-
-
-  - Name: fcn_r101-d8_480x480_80k_pascal_context_59
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 49.35
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
-    Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py

configs/fp16/fp16.yml

@@ -0,0 +1,90 @@
+Collections:
+- Name: fp16
+  Metadata:
+    Training Data:
+    - Cityscapes
+Models:
+- Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
+  In Collection: fp16
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 115.74
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.37
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.8
+  Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth
+- Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
+  In Collection: fp16
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 114.03
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.34
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.46
+  Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth
+- Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
+  In Collection: fp16
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 259.07
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.75
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.48
+  Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth
+- Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
+  In Collection: fp16
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 127.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.35
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.46
+  Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth

configs/fp16/metafile.yml

@@ -1,76 +0,0 @@
-
-Models:
-
-  - Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 115.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth
-    Config: configs/fcn/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
-
-
-
-  - Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 114.03
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.46
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
-
-
-
-  - Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 259.07
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.48
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth
-    Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 127.06
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.46
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth
-    Config: configs/deeplabv3+/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py

configs/gcnet/gcnet.yml

@@ -0,0 +1,296 @@
+Collections:
+- Name: gcnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: gcnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 254.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.69
+      mIoU(ms+flip): 78.56
+  Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
+- Name: gcnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 383.14
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.28
+      mIoU(ms+flip): 79.34
+  Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
+- Name: gcnet_r50-d8_769x769_40k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 598.8
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.12
+      mIoU(ms+flip): 80.09
+  Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
+- Name: gcnet_r101-d8_769x769_40k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 884.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.95
+      mIoU(ms+flip): 80.71
+  Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
+- Name: gcnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.48
+      mIoU(ms+flip): 80.01
+  Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
+- Name: gcnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.03
+      mIoU(ms+flip): 79.84
+  Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
+- Name: gcnet_r50-d8_769x769_80k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.68
+      mIoU(ms+flip): 80.66
+  Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
+- Name: gcnet_r101-d8_769x769_80k_cityscapes
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.18
+      mIoU(ms+flip): 80.71
+  Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
+- Name: gcnet_r50-d8_512x512_80k_ade20k
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 42.77
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.47
+      mIoU(ms+flip): 42.85
+  Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
+- Name: gcnet_r101-d8_512x512_80k_ade20k
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 65.79
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.82
+      mIoU(ms+flip): 44.54
+  Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
+- Name: gcnet_r50-d8_512x512_160k_ade20k
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.37
+      mIoU(ms+flip): 43.52
+  Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
+- Name: gcnet_r101-d8_512x512_160k_ade20k
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.69
+      mIoU(ms+flip): 45.21
+  Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
+- Name: gcnet_r50-d8_512x512_20k_voc12aug
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 42.83
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.42
+      mIoU(ms+flip): 77.51
+  Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
+- Name: gcnet_r101-d8_512x512_20k_voc12aug
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 67.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.41
+      mIoU(ms+flip): 78.56
+  Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
+- Name: gcnet_r50-d8_512x512_40k_voc12aug
+  In Collection: gcnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 77.63
+  Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
+- Name: gcnet_r101-d8_512x512_40k_voc12aug
+  In Collection: gcnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.84
+      mIoU(ms+flip): 78.59
+  Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth

configs/gcnet/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: GCNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: gcnet_r50-d8_512x1024_40k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 254.45
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.69
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
-    Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: gcnet_r101-d8_512x1024_40k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 383.14
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.28
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
-    Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: gcnet_r50-d8_769x769_40k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 598.8
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.12
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
-    Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: gcnet_r101-d8_769x769_40k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 884.96
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.95
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
-    Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: gcnet_r50-d8_512x1024_80k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 254.45
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.48
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
-    Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: gcnet_r101-d8_512x1024_80k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 383.14
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.03
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
-    Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: gcnet_r50-d8_769x769_80k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 598.8
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.68
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
-    Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: gcnet_r101-d8_769x769_80k_cityscapes
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 884.96
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.18
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
-    Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: gcnet_r50-d8_512x512_80k_ade20k
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.77
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
-    Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: gcnet_r101-d8_512x512_80k_ade20k
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 65.79
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.82
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
-    Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: gcnet_r50-d8_512x512_160k_ade20k
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.77
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.37
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
-    Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: gcnet_r101-d8_512x512_160k_ade20k
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 65.79
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.69
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
-    Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: gcnet_r50-d8_512x512_20k_voc12aug
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.42
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
-    Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: gcnet_r101-d8_512x512_20k_voc12aug
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 67.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.41
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
-    Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: gcnet_r50-d8_512x512_40k_voc12aug
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.24
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
-    Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: gcnet_r101-d8_512x512_40k_voc12aug
-    In Collection: GCNet
-    Metadata:
-      inference time (ms/im):
-        - value: 67.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.84
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth
-    Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py

configs/hrnet/hrnet.yml

@@ -0,0 +1,440 @@
+Collections:
+- Name: hrnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+Models:
+- Name: fcn_hr18s_512x1024_40k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 42.12
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.86
+      mIoU(ms+flip): 75.91
+  Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
+- Name: fcn_hr18_512x1024_40k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 77.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 2.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.19
+      mIoU(ms+flip): 78.92
+  Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
+- Name: fcn_hr48_512x1024_40k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 155.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.48
+      mIoU(ms+flip): 79.69
+  Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
+- Name: fcn_hr18s_512x1024_80k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.31
+      mIoU(ms+flip): 77.48
+  Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
+- Name: fcn_hr18_512x1024_80k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.65
+      mIoU(ms+flip): 80.35
+  Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
+- Name: fcn_hr48_512x1024_80k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.93
+      mIoU(ms+flip): 80.72
+  Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
+- Name: fcn_hr18s_512x1024_160k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.31
+      mIoU(ms+flip): 78.31
+  Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
+- Name: fcn_hr18_512x1024_160k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.74
+  Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
+- Name: fcn_hr48_512x1024_160k_cityscapes
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.65
+      mIoU(ms+flip): 81.92
+  Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
+- Name: fcn_hr18s_512x512_80k_ade20k
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 25.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 3.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 31.38
+      mIoU(ms+flip): 32.45
+  Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
+- Name: fcn_hr18_512x512_80k_ade20k
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 44.31
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 4.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 35.51
+      mIoU(ms+flip): 36.8
+  Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth
+- Name: fcn_hr48_512x512_80k_ade20k
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.9
+      mIoU(ms+flip): 43.27
+  Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
+- Name: fcn_hr18s_512x512_160k_ade20k
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 33.0
+      mIoU(ms+flip): 34.55
+  Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth
+- Name: fcn_hr18_512x512_160k_ade20k
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.79
+      mIoU(ms+flip): 38.58
+  Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
+- Name: fcn_hr48_512x512_160k_ade20k
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.02
+      mIoU(ms+flip): 43.86
+  Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
+- Name: fcn_hr18s_512x512_20k_voc12aug
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 23.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 1.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 65.2
+      mIoU(ms+flip): 68.55
+  Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth
+- Name: fcn_hr18_512x512_20k_voc12aug
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 42.59
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 2.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.3
+      mIoU(ms+flip): 74.71
+  Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
+- Name: fcn_hr48_512x512_20k_voc12aug
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 45.35
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.87
+      mIoU(ms+flip): 78.58
+  Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
+- Name: fcn_hr18s_512x512_40k_voc12aug
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 66.61
+      mIoU(ms+flip): 70.0
+  Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
+- Name: fcn_hr18_512x512_40k_voc12aug
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.9
+      mIoU(ms+flip): 75.59
+  Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
+- Name: fcn_hr48_512x512_40k_voc12aug
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 78.49
+  Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
+- Name: fcn_hr48_480x480_40k_pascal_context
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 112.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+    memory (GB): 6.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 45.14
+      mIoU(ms+flip): 47.42
+  Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
+- Name: fcn_hr48_480x480_80k_pascal_context
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 45.84
+      mIoU(ms+flip): 47.84
+  Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
+- Name: fcn_hr48_480x480_40k_pascal_context_59
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 50.33
+      mIoU(ms+flip): 52.83
+  Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
+- Name: fcn_hr48_480x480_80k_pascal_context_59
+  In Collection: hrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 51.12
+      mIoU(ms+flip): 53.56
+  Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth

configs/hrnet/metafile.yml

@@ -1,473 +0,0 @@
-Models:
-  - Name: fcn_hr18s_512x1024_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.12
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 73.86
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
-    Config: configs/fcn/fcn_hr18s_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_hr18_512x1024_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 77.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.19
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
-    Config: configs/fcn/fcn_hr18_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_hr48_512x1024_40k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 155.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.48
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
-    Config: configs/fcn/fcn_hr48_512x1024_40k_cityscapes.py
-
-
-
-  - Name: fcn_hr18s_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.12
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.31
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
-    Config: configs/fcn/fcn_hr18s_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_hr18_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 77.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.65
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
-    Config: configs/fcn/fcn_hr18_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_hr48_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 155.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.93
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
-    Config: configs/fcn/fcn_hr48_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_hr18s_512x1024_160k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.12
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.31
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
-    Config: configs/fcn/fcn_hr18s_512x1024_160k_cityscapes.py
-
-
-
-  - Name: fcn_hr18_512x1024_160k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 77.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
-    Config: configs/fcn/fcn_hr18_512x1024_160k_cityscapes.py
-
-
-
-  - Name: fcn_hr48_512x1024_160k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 155.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.65
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
-    Config: configs/fcn/fcn_hr48_512x1024_160k_cityscapes.py
-
-
-
-  - Name: fcn_hr18s_512x512_80k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 25.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 31.38
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
-    Config: configs/fcn/fcn_hr18s_512x512_80k_ade20k.py
-
-
-
-  - Name: fcn_hr18_512x512_80k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 44.31
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 35.51
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth
-    Config: configs/fcn/fcn_hr18_512x512_80k_ade20k.py
-
-
-
-  - Name: fcn_hr48_512x512_80k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 47.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.90
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
-    Config: configs/fcn/fcn_hr48_512x512_80k_ade20k.py
-
-
-
-  - Name: fcn_hr18s_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 25.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 33.00
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth
-    Config: configs/fcn/fcn_hr18s_512x512_160k_ade20k.py
-
-
-
-  - Name: fcn_hr18_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 44.31
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 36.79
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
-    Config: configs/fcn/fcn_hr18_512x512_160k_ade20k.py
-
-
-
-  - Name: fcn_hr48_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 47.1
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
-    Config: configs/fcn/fcn_hr48_512x512_160k_ade20k.py
-
-
-
-  - Name: fcn_hr18s_512x512_20k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 23.06
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 65.20
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth
-    Config: configs/fcn/fcn_hr18s_512x512_20k_voc12aug.py
-
-
-
-  - Name: fcn_hr18_512x512_20k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.59
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 72.30
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
-    Config: configs/fcn/fcn_hr18_512x512_20k_voc12aug.py
-
-
-
-  - Name: fcn_hr48_512x512_20k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 45.35
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 75.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
-    Config: configs/fcn/fcn_hr48_512x512_20k_voc12aug.py
-
-
-
-  - Name: fcn_hr18s_512x512_40k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 23.06
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 66.61
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
-    Config: configs/fcn/fcn_hr18s_512x512_40k_voc12aug.py
-
-
-
-  - Name: fcn_hr18_512x512_40k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 42.59
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 72.90
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
-    Config: configs/fcn/fcn_hr18_512x512_40k_voc12aug.py
-
-
-
-  - Name: fcn_hr48_512x512_40k_voc12aug
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 45.35
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.24
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
-    Config: configs/fcn/fcn_hr48_512x512_40k_voc12aug.py
-
-
-
-  - Name: fcn_hr48_480x480_40k_pascal_context
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 112.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 45.14
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
-    Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context.py
-
-
-
-  - Name: fcn_hr48_480x480_80k_pascal_context
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 112.87
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 45.84
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
-    Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py
-
-
-
-  - Name: fcn_hr48_480x480_40k_pascal_context_59
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 50.33
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
-    Config: configs/fcn/fcn_hr48_480x480_40k_pascal_context_59.py
-
-
-
-  - Name: fcn_hr48_480x480_80k_pascal_context
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 51.12
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
-    Config: configs/fcn/fcn_hr48_480x480_80k_pascal_context.py

configs/mobilenet_v2/metafile.yml

@@ -1,152 +0,0 @@
-
-Models:
-
-  - Name: fcn_m-v2-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 70.42
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 61.54
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
-    Config: configs/fcn/fcn_m-v2-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 89.29
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 70.23
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
-    Config: configs/pspnet/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 119.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 73.84
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
-    Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 119.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.20
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
-    Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_m-v2-d8_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 15.53
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 19.71
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
-    Config: configs/fcn/fcn_m-v2-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: pspnet_m-v2-d8_512x512_160k_ade20k
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 17.33
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 29.68
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
-    Config: configs/pspnet/pspnet_m-v2-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 25.06
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 34.08
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
-    Config: configs/deeplabv3/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 23.2
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 34.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
-    Config: configs/deeplabv3+/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py

configs/mobilenet_v2/mobilenet_v2.yml

@@ -0,0 +1,175 @@
+Collections:
+- Name: mobilenet_v2
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20k
+Models:
+- Name: fcn_m-v2-d8_512x1024_80k_cityscapes
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 70.42
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 61.54
+  Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
+- Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 89.29
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.23
+  Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
+- Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 119.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.84
+  Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
+- Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 119.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.2
+  Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
+- Name: fcn_m-v2-d8_512x512_160k_ade20k
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 15.53
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 19.71
+  Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
+- Name: pspnet_m-v2-d8_512x512_160k_ade20k
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 17.33
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 29.68
+  Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
+- Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 25.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 34.08
+  Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
+- Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
+  In Collection: mobilenet_v2
+  Metadata:
+    backbone: M-V2-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 23.2
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 34.02
+  Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth

configs/mobilenet_v3/metafile.yml

@@ -1,81 +0,0 @@
-Collections:
-  - Name: LRASPP
-    Metadata:
-      Training Data:
-        - Cityscapes
-
-Models:
-
-  - Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
-    In Collection: LRASPP
-    Metadata:
-      inference time (ms/im):
-        - value: 65.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 69.54
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
-    Config: configs/lraspp/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
-
-
-
-  - Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
-    In Collection: LRASPP
-    Metadata:
-      inference time (ms/im):
-        - value: 67.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 67.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
-    Config: configs/lraspp/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
-
-
-
-  - Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
-    In Collection: LRASPP
-    Metadata:
-      inference time (ms/im):
-        - value: 42.3
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 64.11
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
-    Config: configs/lraspp/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
-
-
-
-  - Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
-    In Collection: LRASPP
-    Metadata:
-      inference time (ms/im):
-        - value: 40.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 62.74
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
-    Config: configs/lraspp/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py

configs/mobilenet_v3/mobilenet_v3.yml

@@ -0,0 +1,94 @@
+Collections:
+- Name: mobilenet_v3
+  Metadata:
+    Training Data:
+    - Cityscapes
+Models:
+- Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
+  In Collection: mobilenet_v3
+  Metadata:
+    backbone: M-V3-D8
+    crop size: (512,1024)
+    lr schd: 320000
+    inference time (ms/im):
+    - value: 65.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 69.54
+      mIoU(ms+flip): 70.89
+  Config: configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
+- Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
+  In Collection: mobilenet_v3
+  Metadata:
+    backbone: M-V3-D8 (scratch)
+    crop size: (512,1024)
+    lr schd: 320000
+    inference time (ms/im):
+    - value: 67.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 67.87
+      mIoU(ms+flip): 69.78
+  Config: configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
+- Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
+  In Collection: mobilenet_v3
+  Metadata:
+    backbone: M-V3s-D8
+    crop size: (512,1024)
+    lr schd: 320000
+    inference time (ms/im):
+    - value: 42.3
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 64.11
+      mIoU(ms+flip): 66.42
+  Config: configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
+- Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
+  In Collection: mobilenet_v3
+  Metadata:
+    backbone: M-V3s-D8 (scratch)
+    crop size: (512,1024)
+    lr schd: 320000
+    inference time (ms/im):
+    - value: 40.82
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 5.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 62.74
+      mIoU(ms+flip): 65.01
+  Config: configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth

configs/nonlocal_net/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: NonLocal
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: nonlocal_r50-d8_512x1024_40k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 367.65
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.24
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: nonlocal_r101-d8_512x1024_40k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 512.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.66
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: nonlocal_r50-d8_769x769_40k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 657.89
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.33
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: nonlocal_r101-d8_769x769_40k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 952.38
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.57
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: nonlocal_r50-d8_512x1024_80k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 367.65
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.01
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: nonlocal_r101-d8_512x1024_80k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 512.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.93
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: nonlocal_r50-d8_769x769_80k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 657.89
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.05
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: nonlocal_r101-d8_769x769_80k_cityscapes
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 952.38
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.40
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: nonlocal_r50-d8_512x512_80k_ade20k
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 46.79
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 40.75
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: nonlocal_r101-d8_512x512_80k_ade20k
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 71.58
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.90
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: nonlocal_r50-d8_512x512_160k_ade20k
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 46.79
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.03
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: nonlocal_r101-d8_512x512_160k_ade20k
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 71.58
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.36
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: nonlocal_r50-d8_512x512_20k_voc12aug
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 47.15
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.20
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: nonlocal_r101-d8_512x512_20k_voc12aug
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 71.38
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 78.15
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: nonlocal_r50-d8_512x512_40k_voc12aug
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 47.15
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.65
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
-    Config: configs/nonlocal/nonlocal_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: nonlocal_r101-d8_512x512_40k_voc12aug
-    In Collection: NonLocal
-    Metadata:
-      inference time (ms/im):
-        - value: 71.38
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 78.27
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth
-    Config: configs/nonlocal/nonlocal_r101-d8_512x512_40k_voc12aug.py

configs/nonlocal_net/nonlocal_net.yml

@@ -0,0 +1,292 @@
+Collections:
+- Name: nonlocal_net
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: nonlocal_r50-d8_512x1024_40k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 367.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.24
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
+- Name: nonlocal_r101-d8_512x1024_40k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 512.82
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 10.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.66
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
+- Name: nonlocal_r50-d8_769x769_40k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 657.89
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.33
+      mIoU(ms+flip): 79.92
+  Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
+- Name: nonlocal_r101-d8_769x769_40k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 952.38
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 12.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.57
+      mIoU(ms+flip): 80.29
+  Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
+- Name: nonlocal_r50-d8_512x1024_80k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.01
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
+- Name: nonlocal_r101-d8_512x1024_80k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.93
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
+- Name: nonlocal_r50-d8_769x769_80k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.05
+      mIoU(ms+flip): 80.68
+  Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
+- Name: nonlocal_r101-d8_769x769_80k_cityscapes
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.4
+      mIoU(ms+flip): 80.85
+  Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
+- Name: nonlocal_r50-d8_512x512_80k_ade20k
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 46.79
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.75
+      mIoU(ms+flip): 42.05
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
+- Name: nonlocal_r101-d8_512x512_80k_ade20k
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 71.58
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.9
+      mIoU(ms+flip): 44.27
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
+- Name: nonlocal_r50-d8_512x512_160k_ade20k
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.03
+      mIoU(ms+flip): 43.04
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
+- Name: nonlocal_r101-d8_512x512_160k_ade20k
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.36
+      mIoU(ms+flip): 44.83
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth
+- Name: nonlocal_r50-d8_512x512_20k_voc12aug
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 47.15
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.2
+      mIoU(ms+flip): 77.12
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
+- Name: nonlocal_r101-d8_512x512_20k_voc12aug
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 71.38
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.15
+      mIoU(ms+flip): 78.86
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
+- Name: nonlocal_r50-d8_512x512_40k_voc12aug
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.65
+      mIoU(ms+flip): 77.47
+  Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
+- Name: nonlocal_r101-d8_512x512_40k_voc12aug
+  In Collection: nonlocal_net
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.27
+      mIoU(ms+flip): 79.12
+  Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth

configs/ocrnet/metafile.yml

@@ -1,463 +0,0 @@
-Collections:
-  - Name: OCRNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: ocrnet_hr18s_512x1024_40k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 95.69
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 74.30
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr18_512x1024_40k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 133.33
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.72
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr48_512x1024_40k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 236.97
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.58
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr18s_512x1024_80k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 95.69
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.16
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr18_512x1024_80k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 133.33
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.57
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr48_512x1024_80k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 236.97
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.70
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr18s_512x1024_160k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 95.69
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.45
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr18_512x1024_160k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 133.33
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
-
-
-
-  - Name: ocrnet_hr48_512x1024_160k_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 236.97
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 81.35
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
-
-
-
-  - Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU:
-    Weights: https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
-    Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
-
-
-
-  - Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 113.64
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 3.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth
-    Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
-
-
-
-  - Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 113.64
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 3.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth
-    Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
-
-
-
-  - Name: ocrnet_hr18s_512x512_80k_ade20k
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 34.51
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 35.06
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
-
-
-
-  - Name: ocrnet_hr18_512x512_80k_ade20k
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 52.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 37.79
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
-
-
-
-  - Name: ocrnet_hr48_512x512_80k_ade20k
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 58.86
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.00
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
-
-
-
-  - Name: ocrnet_hr18s_512x512_160k_ade20k
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 34.51
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 37.19
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
-
-
-
-  - Name: ocrnet_hr18_512x512_160k_ade20k
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 52.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 39.32
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
-
-
-
-  - Name: ocrnet_hr48_512x512_160k_ade20k
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 58.86
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.25
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
-
-
-
-  - Name: ocrnet_hr18s_512x512_20k_voc12aug
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 31.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 71.70
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
-
-
-
-  - Name: ocrnet_hr18_512x512_20k_voc12aug
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 50.23
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 74.75
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
-
-
-
-  - Name: ocrnet_hr48_512x512_20k_voc12aug
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 56.09
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.72
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
-
-
-
-  - Name: ocrnet_hr18s_512x512_40k_voc12aug
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 31.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 72.76
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
-    Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
-
-
-
-  - Name: ocrnet_hr18_512x512_40k_voc12aug
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 50.23
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 74.98
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
-    Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
-
-
-
-  - Name: ocrnet_hr48_512x512_40k_voc12aug
-    In Collection: OCRNet
-    Metadata:
-      inference time (ms/im):
-        - value: 56.09
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.14
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
-    Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py

configs/ocrnet/ocrnet.yml

@@ -0,0 +1,431 @@
+Collections:
+- Name: ocrnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ' HRNet backbone'
+    - ' ResNet backbone'
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: ocrnet_hr18s_512x1024_40k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 95.69
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 74.3
+      mIoU(ms+flip): 75.95
+  Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
+- Name: ocrnet_hr18_512x1024_40k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 133.33
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 4.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 77.72
+      mIoU(ms+flip): 79.49
+  Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
+- Name: ocrnet_hr48_512x1024_40k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 236.97
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 80.58
+      mIoU(ms+flip): 81.79
+  Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
+- Name: ocrnet_hr18s_512x1024_80k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 77.16
+      mIoU(ms+flip): 78.66
+  Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
+- Name: ocrnet_hr18_512x1024_80k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 78.57
+      mIoU(ms+flip): 80.46
+  Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
+- Name: ocrnet_hr48_512x1024_80k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 80.7
+      mIoU(ms+flip): 81.87
+  Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
+- Name: ocrnet_hr18s_512x1024_160k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 78.45
+      mIoU(ms+flip): 79.97
+  Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
+- Name: ocrnet_hr18_512x1024_160k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 79.47
+      mIoU(ms+flip): 80.91
+  Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
+- Name: ocrnet_hr48_512x1024_160k_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' HRNet backbone'
+    Metrics:
+      mIoU: 81.35
+      mIoU(ms+flip): 82.7
+  Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
+- Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' ResNet backbone'
+    Metrics:
+      mIoU: 80.09
+  Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth
+- Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 331.13
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' ResNet backbone'
+    Metrics:
+      mIoU: 80.3
+  Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth
+- Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
+  In Collection: ocrnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 331.13
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 8.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ' ResNet backbone'
+    Metrics:
+      mIoU: 80.81
+  Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes-78688424.pth
+- Name: ocrnet_hr18s_512x512_80k_ade20k
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 34.51
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 35.06
+      mIoU(ms+flip): 35.8
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
+- Name: ocrnet_hr18_512x512_80k_ade20k
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 52.83
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.79
+      mIoU(ms+flip): 39.16
+  Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
+- Name: ocrnet_hr48_512x512_80k_ade20k
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 58.86
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 11.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.0
+      mIoU(ms+flip): 44.3
+  Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
+- Name: ocrnet_hr18s_512x512_160k_ade20k
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.19
+      mIoU(ms+flip): 38.4
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
+- Name: ocrnet_hr18_512x512_160k_ade20k
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.32
+      mIoU(ms+flip): 40.8
+  Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
+- Name: ocrnet_hr48_512x512_160k_ade20k
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.25
+      mIoU(ms+flip): 44.88
+  Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
+- Name: ocrnet_hr18s_512x512_20k_voc12aug
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 31.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 3.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 71.7
+      mIoU(ms+flip): 73.84
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
+- Name: ocrnet_hr18_512x512_20k_voc12aug
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 50.23
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 4.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.75
+      mIoU(ms+flip): 77.11
+  Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
+- Name: ocrnet_hr48_512x512_20k_voc12aug
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 56.09
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.72
+      mIoU(ms+flip): 79.87
+  Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
+- Name: ocrnet_hr18s_512x512_40k_voc12aug
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.76
+      mIoU(ms+flip): 74.6
+  Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
+- Name: ocrnet_hr18_512x512_40k_voc12aug
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.98
+      mIoU(ms+flip): 77.4
+  Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
+- Name: ocrnet_hr48_512x512_40k_voc12aug
+  In Collection: ocrnet
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.14
+      mIoU(ms+flip): 79.71
+  Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth

configs/point_rend/metafile.yml

@@ -1,82 +0,0 @@
-Collections:
-  - Name: PointRend
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - ADE20K
-
-Models:
-
-  - Name: pointrend_r50_512x1024_80k_cityscapes
-    In Collection: PointRend
-    Metadata:
-      inference time (ms/im):
-        - value: 117.92
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 76.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
-    Config: configs/pointrend/pointrend_r50_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pointrend_r101_512x1024_80k_cityscapes
-    In Collection: PointRend
-    Metadata:
-      inference time (ms/im):
-        - value: 142.86
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.30
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
-    Config: configs/pointrend/pointrend_r101_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pointrend_r50_512x512_160k_ade20k
-    In Collection: PointRend
-    Metadata:
-      inference time (ms/im):
-        - value: 57.77
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 37.64
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
-    Config: configs/pointrend/pointrend_r50_512x512_160k_ade20k.py
-
-
-
-  - Name: pointrend_r101_512x512_160k_ade20k
-    In Collection: PointRend
-    Metadata:
-      inference time (ms/im):
-        - value: 64.52
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 40.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
-    Config: configs/pointrend/pointrend_r101_512x512_160k_ade20k.py

configs/point_rend/point_rend.yml

@@ -0,0 +1,95 @@
+Collections:
+- Name: point_rend
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+Models:
+- Name: pointrend_r50_512x1024_80k_cityscapes
+  In Collection: point_rend
+  Metadata:
+    backbone: R-50
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 117.92
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.47
+      mIoU(ms+flip): 78.13
+  Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
+- Name: pointrend_r101_512x1024_80k_cityscapes
+  In Collection: point_rend
+  Metadata:
+    backbone: R-101
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 142.86
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 4.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.3
+      mIoU(ms+flip): 79.97
+  Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
+- Name: pointrend_r50_512x512_160k_ade20k
+  In Collection: point_rend
+  Metadata:
+    backbone: R-50
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 57.77
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.64
+      mIoU(ms+flip): 39.17
+  Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
+- Name: pointrend_r101_512x512_160k_ade20k
+  In Collection: point_rend
+  Metadata:
+    backbone: R-101
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 64.52
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.02
+      mIoU(ms+flip): 41.6
+  Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth

configs/psanet/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: PSANet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: psanet_r50-d8_512x1024_40k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 315.46
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.63
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
-    Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: psanet_r101-d8_512x1024_40k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 454.55
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.14
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
-    Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: psanet_r50-d8_769x769_40k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 714.29
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.99
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
-    Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: psanet_r101-d8_769x769_40k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 1020.41
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.43
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
-    Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: psanet_r50-d8_512x1024_80k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 315.46
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.24
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
-    Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: psanet_r101-d8_512x1024_80k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 454.55
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.31
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
-    Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: psanet_r50-d8_769x769_80k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 714.29
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.31
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
-    Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: psanet_r101-d8_769x769_80k_cityscapes
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 1020.41
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.69
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
-    Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: psanet_r50-d8_512x512_80k_ade20k
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 52.88
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.14
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
-    Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: psanet_r101-d8_512x512_80k_ade20k
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 76.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
-    Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: psanet_r50-d8_512x512_160k_ade20k
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 52.88
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.67
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
-    Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: psanet_r101-d8_512x512_160k_ade20k
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 76.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.74
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
-    Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: psanet_r50-d8_512x512_20k_voc12aug
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 54.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.39
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
-    Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: psanet_r101-d8_512x512_20k_voc12aug
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 79.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.91
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
-    Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: psanet_r50-d8_512x512_40k_voc12aug
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 54.82
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.30
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
-    Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: psanet_r101-d8_512x512_40k_voc12aug
-    In Collection: PSANet
-    Metadata:
-      inference time (ms/im):
-        - value: 79.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.73
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
-    Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py

configs/psanet/psanet.yml

@@ -0,0 +1,296 @@
+Collections:
+- Name: psanet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: psanet_r50-d8_512x1024_40k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 315.46
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.63
+      mIoU(ms+flip): 79.04
+  Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
+- Name: psanet_r101-d8_512x1024_40k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 454.55
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 10.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.14
+      mIoU(ms+flip): 80.19
+  Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
+- Name: psanet_r50-d8_769x769_40k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 714.29
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 7.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.99
+      mIoU(ms+flip): 79.64
+  Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
+- Name: psanet_r101-d8_769x769_40k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 1020.41
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 11.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.43
+      mIoU(ms+flip): 80.26
+  Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
+- Name: psanet_r50-d8_512x1024_80k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.24
+      mIoU(ms+flip): 78.69
+  Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
+- Name: psanet_r101-d8_512x1024_80k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.31
+      mIoU(ms+flip): 80.53
+  Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
+- Name: psanet_r50-d8_769x769_80k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.31
+      mIoU(ms+flip): 80.91
+  Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
+- Name: psanet_r101-d8_769x769_80k_cityscapes
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.69
+      mIoU(ms+flip): 80.89
+  Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
+- Name: psanet_r50-d8_512x512_80k_ade20k
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 52.88
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.14
+      mIoU(ms+flip): 41.91
+  Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
+- Name: psanet_r101-d8_512x512_80k_ade20k
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 76.16
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.8
+      mIoU(ms+flip): 44.75
+  Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
+- Name: psanet_r50-d8_512x512_160k_ade20k
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.67
+      mIoU(ms+flip): 42.95
+  Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
+- Name: psanet_r101-d8_512x512_160k_ade20k
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.74
+      mIoU(ms+flip): 45.38
+  Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
+- Name: psanet_r50-d8_512x512_20k_voc12aug
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 54.82
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.39
+      mIoU(ms+flip): 77.34
+  Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
+- Name: psanet_r101-d8_512x512_20k_voc12aug
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 79.18
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 10.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.91
+      mIoU(ms+flip): 79.3
+  Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
+- Name: psanet_r50-d8_512x512_40k_voc12aug
+  In Collection: psanet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.3
+      mIoU(ms+flip): 77.35
+  Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
+- Name: psanet_r101-d8_512x512_40k_voc12aug
+  In Collection: psanet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.73
+      mIoU(ms+flip): 79.05
+  Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth

configs/pspnet/metafile.yml

@@ -1,540 +0,0 @@
-Collections:
-  - Name: PSPNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal Context
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: pspnet_r50-d8_512x1024_40k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 245.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.85
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
-    Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: pspnet_r101-d8_512x1024_40k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 373.13
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.34
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
-
-
-
-  - Name: pspnet_r50-d8_769x769_40k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 568.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.26
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
-    Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: pspnet_r101-d8_769x769_40k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.08
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
-    Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
-
-
-
-  - Name: pspnet_r18-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 63.65
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 74.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
-    Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r50-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 245.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.55
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
-    Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r101-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 373.13
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.76
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r18-d8_769x769_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 161.29
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.90
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
-    Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r50-d8_769x769_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 568.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.59
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
-    Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r101-d8_769x769_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 869.57
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.77
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
-    Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r18b-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 61.43
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 74.23
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
-    Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r50b-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 232.56
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.22
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
-    Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r101b-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 362.32
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.69
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
-    Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r18b-d8_769x769_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 156.01
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 74.92
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
-    Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r50b-d8_769x769_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 531.91
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.50
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
-    Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r101b-d8_769x769_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 854.7
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.87
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
-    Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
-
-
-
-  - Name: pspnet_r50-d8_512x512_80k_ade20k
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.5
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 41.13
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
-    Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: pspnet_r101-d8_512x512_80k_ade20k
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 65.36
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.57
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
-
-
-
-  - Name: pspnet_r50-d8_512x512_160k_ade20k
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.5
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.48
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
-    Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: pspnet_r101-d8_512x512_160k_ade20k
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 65.36
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 44.39
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: pspnet_r50-d8_512x512_20k_voc12aug
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.39
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 76.78
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
-    Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: pspnet_r101-d8_512x512_20k_voc12aug
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 66.58
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 78.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
-
-
-
-  - Name: pspnet_r50-d8_512x512_40k_voc12aug
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.39
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.29
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
-    Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: pspnet_r101-d8_512x512_40k_voc12aug
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 66.58
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 78.52
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
-    Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
-
-
-
-  - Name: pspnet_r101-d8_480x480_40k_pascal_context
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 103.31
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 46.60
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
-    Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: pspnet_r101-d8_480x480_80k_pascal_context
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 103.31
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 46.03
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
-    Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
-
-
-
-  - Name: pspnet_r101-d8_480x480_40k_pascal_context
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 52.02
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
-    Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
-
-
-
-  - Name: pspnet_r101-d8_480x480_80k_pascal_context_59
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal Context
-        Metrics:
-          mIoU: 52.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
-    Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py

configs/pspnet/pspnet.yml

@@ -0,0 +1,538 @@
+Collections:
+- Name: pspnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+Models:
+- Name: pspnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 245.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.85
+      mIoU(ms+flip): 79.18
+  Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
+- Name: pspnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 373.13
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.34
+      mIoU(ms+flip): 79.74
+  Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
+- Name: pspnet_r50-d8_769x769_40k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 568.18
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.26
+      mIoU(ms+flip): 79.88
+  Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
+- Name: pspnet_r101-d8_769x769_40k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 869.57
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.08
+      mIoU(ms+flip): 80.28
+  Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
+- Name: pspnet_r18-d8_512x1024_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 63.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.87
+      mIoU(ms+flip): 76.04
+  Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
+- Name: pspnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.55
+      mIoU(ms+flip): 79.79
+  Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
+- Name: pspnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.76
+      mIoU(ms+flip): 81.01
+  Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
+- Name: pspnet_r18-d8_769x769_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 161.29
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 1.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.9
+      mIoU(ms+flip): 77.86
+  Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
+- Name: pspnet_r50-d8_769x769_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.59
+      mIoU(ms+flip): 80.69
+  Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
+- Name: pspnet_r101-d8_769x769_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.77
+      mIoU(ms+flip): 81.06
+  Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
+- Name: pspnet_r18b-d8_512x1024_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 61.43
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 1.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.23
+      mIoU(ms+flip): 75.79
+  Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
+- Name: pspnet_r50b-d8_512x1024_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 232.56
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.22
+      mIoU(ms+flip): 79.46
+  Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
+- Name: pspnet_r101b-d8_512x1024_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 362.32
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 9.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.69
+      mIoU(ms+flip): 80.79
+  Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
+- Name: pspnet_r18b-d8_769x769_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-18b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 156.01
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 1.7
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.92
+      mIoU(ms+flip): 76.9
+  Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
+- Name: pspnet_r50b-d8_769x769_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 531.91
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 6.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.5
+      mIoU(ms+flip): 79.96
+  Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
+- Name: pspnet_r101b-d8_769x769_80k_cityscapes
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101b-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 854.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 10.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.87
+      mIoU(ms+flip): 80.04
+  Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
+- Name: pspnet_r50-d8_512x512_80k_ade20k
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 42.5
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.13
+      mIoU(ms+flip): 41.94
+  Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
+- Name: pspnet_r101-d8_512x512_80k_ade20k
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 65.36
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 12.0
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.57
+      mIoU(ms+flip): 44.35
+  Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
+- Name: pspnet_r50-d8_512x512_160k_ade20k
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.48
+      mIoU(ms+flip): 43.44
+  Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
+- Name: pspnet_r101-d8_512x512_160k_ade20k
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.39
+      mIoU(ms+flip): 45.35
+  Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
+- Name: pspnet_r50-d8_512x512_20k_voc12aug
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 42.39
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.78
+      mIoU(ms+flip): 77.61
+  Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
+- Name: pspnet_r101-d8_512x512_20k_voc12aug
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 66.58
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.47
+      mIoU(ms+flip): 79.25
+  Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
+- Name: pspnet_r50-d8_512x512_40k_voc12aug
+  In Collection: pspnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.29
+      mIoU(ms+flip): 78.48
+  Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
+- Name: pspnet_r101-d8_512x512_40k_voc12aug
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.52
+      mIoU(ms+flip): 79.57
+  Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
+- Name: pspnet_r101-d8_480x480_40k_pascal_context
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 103.31
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+    memory (GB): 8.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.6
+      mIoU(ms+flip): 47.78
+  Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
+- Name: pspnet_r101-d8_480x480_80k_pascal_context
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.03
+      mIoU(ms+flip): 47.15
+  Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
+- Name: pspnet_r101-d8_480x480_40k_pascal_context_59
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.02
+      mIoU(ms+flip): 53.54
+  Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
+- Name: pspnet_r101-d8_480x480_80k_pascal_context_59
+  In Collection: pspnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.47
+      mIoU(ms+flip): 53.99
+  Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth

configs/resnest/metafile.yml

@@ -1,158 +0,0 @@
-Collections:
-  - Name: ResNeSt
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - ADE20K
-
-Models:
-
-  - Name: fcn_s101-d8_512x1024_80k_cityscapes
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 418.41
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.56
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
-    Config: configs/fcn/fcn_s101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: pspnet_s101-d8_512x1024_80k_cityscapes
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 396.83
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.57
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
-    Config: configs/pspnet/pspnet_s101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 531.91
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.67
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
-    Config: configs/deeplabv3/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 423.73
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.62
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
-    Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fcn_s101-d8_512x512_160k_ade20k
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: 77.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.62
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
-    Config: configs/fcn/fcn_s101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: pspnet_s101-d8_512x512_160k_ade20k
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: 76.8
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.44
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
-    Config: configs/pspnet/pspnet_s101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3_s101-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: 107.76
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 45.71
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
-    Config: configs/deeplabv3/deeplabv3_s101-d8_512x512_160k_ade20k.py
-
-
-
-  - Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
-    In Collection: DeepLabV3+
-    Metadata:
-      inference time (ms/im):
-        - value: 83.61
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 46.47
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
-    Config: configs/deeplabv3+/deeplabv3plus_s101-d8_512x512_160k_ade20k.py

configs/resnest/resnest.yml

@@ -0,0 +1,183 @@
+Collections:
+- Name: resnest
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20k
+Models:
+- Name: fcn_s101-d8_512x1024_80k_cityscapes
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 418.41
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 11.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.56
+      mIoU(ms+flip): 78.98
+  Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
+- Name: pspnet_s101-d8_512x1024_80k_cityscapes
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 396.83
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 11.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.57
+      mIoU(ms+flip): 79.19
+  Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
+- Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 531.91
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 11.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.67
+      mIoU(ms+flip): 80.51
+  Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
+- Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 423.73
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 13.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.62
+      mIoU(ms+flip): 80.27
+  Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
+- Name: fcn_s101-d8_512x512_160k_ade20k
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 77.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 14.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 45.62
+      mIoU(ms+flip): 46.16
+  Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
+- Name: pspnet_s101-d8_512x512_160k_ade20k
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 76.8
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 14.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 45.44
+      mIoU(ms+flip): 46.28
+  Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
+- Name: deeplabv3_s101-d8_512x512_160k_ade20k
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 107.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 14.6
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 45.71
+      mIoU(ms+flip): 46.59
+  Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
+- Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
+  In Collection: resnest
+  Metadata:
+    backbone: S-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 83.61
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 16.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20k
+    Metrics:
+      mIoU: 46.47
+      mIoU(ms+flip): 47.27
+  Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth

configs/sem_fpn/metafile.yml

@@ -1,83 +0,0 @@
-Collections:
-  - Name: FPN
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: fpn_r50_512x1024_80k_cityscapes
-    In Collection: FPN
-    Metadata:
-      inference time (ms/im):
-        - value: 73.86
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 74.52
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
-    Config: configs/fpn/fpn_r50_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fpn_r101_512x1024_80k_cityscapes
-    In Collection: FPN
-    Metadata:
-      inference time (ms/im):
-        - value: 97.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 75.80
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
-    Config: configs/fpn/fpn_r101_512x1024_80k_cityscapes.py
-
-
-
-  - Name: fpn_r50_512x512_160k_ade20k
-    In Collection: FPN
-    Metadata:
-      inference time (ms/im):
-        - value: 17.93
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 37.49
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
-    Config: configs/fpn/fpn_r50_512x512_160k_ade20k.py
-
-
-
-  - Name: fpn_r101_512x512_160k_ade20k
-    In Collection: FPN
-    Metadata:
-      inference time (ms/im):
-        - value: 24.64
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 39.35
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
-    Config: configs/fpn/fpn_r101_512x512_160k_ade20k.py

configs/sem_fpn/sem_fpn.yml

@@ -0,0 +1,95 @@
+Collections:
+- Name: sem_fpn
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+Models:
+- Name: fpn_r50_512x1024_80k_cityscapes
+  In Collection: sem_fpn
+  Metadata:
+    backbone: R-50
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 73.86
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 2.8
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.52
+      mIoU(ms+flip): 76.08
+  Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
+- Name: fpn_r101_512x1024_80k_cityscapes
+  In Collection: sem_fpn
+  Metadata:
+    backbone: R-101
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 97.18
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 3.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.8
+      mIoU(ms+flip): 77.4
+  Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
+- Name: fpn_r50_512x512_160k_ade20k
+  In Collection: sem_fpn
+  Metadata:
+    backbone: R-50
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 17.93
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 4.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.49
+      mIoU(ms+flip): 39.09
+  Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
+- Name: fpn_r101_512x512_160k_ade20k
+  In Collection: sem_fpn
+  Metadata:
+    backbone: R-101
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 24.64
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.9
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.35
+      mIoU(ms+flip): 40.72
+  Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth

configs/setr/setr.yml

@@ -0,0 +1,87 @@
+Collections:
+- Name: setr
+  Metadata:
+    Training Data:
+    - ADE20K
+Models:
+- Name: setr_naive_512x512_160k_b16_ade20k
+  In Collection: setr
+  Metadata:
+    backbone: ViT-L
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 211.86
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 18.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.28
+      mIoU(ms+flip): 49.56
+  Config: configs/setr/setr_naive_512x512_160k_b16_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth
+- Name: setr_pup_512x512_160k_b16_ade20k
+  In Collection: setr
+  Metadata:
+    backbone: ViT-L
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 222.22
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 19.54
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.24
+      mIoU(ms+flip): 49.99
+  Config: configs/setr/setr_pup_512x512_160k_b16_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth
+- Name: setr_mla_512x512_160k_b8_ade20k
+  In Collection: setr
+  Metadata:
+    backbone: ViT-L
+    crop size: (512,512)
+    lr schd: 160000
+    memory (GB): 10.96
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.34
+      mIoU(ms+flip): 49.05
+  Config: configs/setr/setr_mla_512x512_160k_b8_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth
+- Name: setr_mla_512x512_160k_b16_ade20k
+  In Collection: setr
+  Metadata:
+    backbone: ViT-L
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 190.48
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 17.3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.54
+      mIoU(ms+flip): 49.37
+  Config: configs/setr/setr_mla_512x512_160k_b16_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth

configs/swin/swin.yml

@@ -0,0 +1,122 @@
+Collections:
+- Name: swin
+  Metadata:
+    Training Data:
+    - ADE20K
+Models:
+- Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
+  In Collection: swin
+  Metadata:
+    backbone: Swin-T
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 47.48
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.02
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.41
+      mIoU(ms+flip): 45.79
+  Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
+- Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
+  In Collection: swin
+  Metadata:
+    backbone: Swin-S
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 67.93
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.17
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.72
+      mIoU(ms+flip): 49.24
+  Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
+- Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
+  In Collection: swin
+  Metadata:
+    backbone: Swin-B
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 79.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.61
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.99
+      mIoU(ms+flip): 49.57
+  Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
+- Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
+  In Collection: swin
+  Metadata:
+    backbone: Swin-B
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.31
+      mIoU(ms+flip): 51.9
+  Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
+- Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
+  In Collection: swin
+  Metadata:
+    backbone: Swin-B
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 82.64
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.52
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.35
+      mIoU(ms+flip): 49.65
+  Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth
+- Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
+  In Collection: swin
+  Metadata:
+    backbone: Swin-B
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.76
+      mIoU(ms+flip): 52.4
+  Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth

configs/unet/README.md

@@ -19,32 +19,32 @@
 
 ### DRIVE
 
-| Backbone    | Head      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                             |
+|  Method   | Backbone      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                             |
 | ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UNet-S5-D16 | FCN       | 584x565    | 64x64     |  42x42 | 40000   | 0.680    |              - | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive-20201223_191051.log.json)                         |
-| UNet-S5-D16 | PSPNet    | 584x565    | 64x64     |  42x42 | 40000   | 0.599    |              - | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json)             |
-| UNet-S5-D16 | DeepLabV3 | 584x565    | 64x64     |  42x42 | 40000   | 0.596    |              - | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
+|  FCN |  UNet-S5-D16      | 584x565    | 64x64     |  42x42 | 40000   | 0.680    |              - | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive-20201223_191051.log.json)                         |
+|  PSPNet |  UNet-S5-D16   | 584x565    | 64x64     |  42x42 | 40000   | 0.599    |              - | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json)             |
+|  DeepLabV3 | UNet-S5-D16  | 584x565    | 64x64     |  42x42 | 40000   | 0.596    |              - | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
 
 ### STARE
 
-| Backbone    | Head      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
+|   Method   | Backbone      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
 | ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| UNet-S5-D16 | FCN       | 605x700    | 128x128   |  85x85 | 40000   | 0.968    |              - | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json)                         |
-| UNet-S5-D16 | PSPNet    | 605x700    | 128x128   |  85x85 | 40000   | 0.982    |              - | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json)             |
-| UNet-S5-D16 | DeepLabV3 | 605x700    | 128x128   |  85x85 | 40000   | 0.999    |              - | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
+|  FCN |   UNet-S5-D16     | 605x700    | 128x128   |  85x85 | 40000   | 0.968    |              - | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json)                         |
+|  PSPNet |  UNet-S5-D16   | 605x700    | 128x128   |  85x85 | 40000   | 0.982    |              - | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json)             |
+|  DeepLabV3 | UNet-S5-D16  | 605x700    | 128x128   |  85x85 | 40000   | 0.999    |              - | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
 
 ### CHASE_DB1
 
-| Backbone    | Head      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
+| Method    | Backbone      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
 | ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| UNet-S5-D16 | FCN       | 960x999    | 128x128   |  85x85 | 40000   | 0.968    |              - | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json)                         |
-| UNet-S5-D16 | PSPNet    | 960x999    | 128x128   |  85x85 | 40000   | 0.982    |              - | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json)             |
-| UNet-S5-D16 | DeepLabV3 | 960x999    | 128x128   |  85x85 | 40000   | 0.999    |              - | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) |
+|  FCN |   UNet-S5-D16     | 960x999    | 128x128   |  85x85 | 40000   | 0.968    |              - | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json)                         |
+|  PSPNet | UNet-S5-D16    | 960x999    | 128x128   |  85x85 | 40000   | 0.982    |              - | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json)             |
+| DeepLabV3 | UNet-S5-D16 | 960x999    | 128x128   |  85x85 | 40000   | 0.999    |              - | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) |
 
 ### HRF
 
-| Backbone    | Head      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
+|  Method    | Backbone      | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) |  Dice | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                     |
 | ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| UNet-S5-D16 | FCN       | 2336x3504  | 256x256   | 170x170 | 40000   | 2.525    |              - | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json)                         |
-| UNet-S5-D16 | PSPNet    | 2336x3504  | 256x256   | 170x170 | 40000   | 2.588    |              - | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json)             |
-| UNet-S5-D16 | DeepLabV3 | 2336x3504  | 256x256   | 170x170 | 40000   | 2.604    |              - | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |
+|  FCN |    UNet-S5-D16    | 2336x3504  | 256x256   | 170x170 | 40000   | 2.525    |              - | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json)                         |
+|  PSPNet |  UNet-S5-D16   | 2336x3504  | 256x256   | 170x170 | 40000   | 2.588    |              - | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json)             |
+|  DeepLabV3 | UNet-S5-D16 | 2336x3504  | 256x256   | 170x170 | 40000   | 2.604    |              - | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |

configs/unet/metafile.yml

@@ -1,227 +0,0 @@
-Models:
-
-  - Name: fcn_unet_s5-d16_64x64_40k_drive
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: DRIVE
-        Metrics:
-          mIoU: 0.680
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth
-    Config: configs/unet-s5-d16/fcn_unet_s5-d16_64x64_40k_drive.py
-
-
-
-  - Name: pspnet_unet_s5-d16_64x64_40k_drive
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: DRIVE
-        Metrics:
-          mIoU: 0.599
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
-    Config: configs/unet-s5-d16/pspnet_unet_s5-d16_64x64_40k_drive.py
-
-
-
-  - Name: deeplabv3_unet_s5-d16_64x64_40k_drive
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: DRIVE
-        Metrics:
-          mIoU: 0.596
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
-    Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_64x64_40k_drive.py
-
-
-
-  - Name: fcn_unet_s5-d16_128x128_40k_stare
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: STARE
-        Metrics:
-          mIoU: 0.968
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth
-    Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_stare.py
-
-
-
-  - Name: pspnet_unet_s5-d16_128x128_40k_stare
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: STARE
-        Metrics:
-          mIoU: 0.982
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
-    Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_stare.py
-
-
-
-  - Name: deeplabv3_unet_s5-d16_128x128_40k_stare
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: STARE
-        Metrics:
-          mIoU: 0.999
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
-    Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_stare.py
-
-
-
-  - Name: fcn_unet_s5-d16_128x128_40k_chase_db1
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: CHASE_DB1
-        Metrics:
-          mIoU: 0.968
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth
-    Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_chase_db1.py
-
-
-
-  - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: CHASE_DB1
-        Metrics:
-          mIoU: 0.982
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
-    Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
-
-
-
-  - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: CHASE_DB1
-        Metrics:
-          mIoU: 0.999
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
-    Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
-
-
-
-  - Name: fcn_unet_s5-d16_256x256_40k_hrf
-    In Collection: FCN
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: HRF
-        Metrics:
-          mIoU: 2.525
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth
-    Config: configs/unet-s5-d16/fcn_unet_s5-d16_256x256_40k_hrf.py
-
-
-
-  - Name: pspnet_unet_s5-d16_256x256_40k_hrf
-    In Collection: PSPNet
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: HRF
-        Metrics:
-          mIoU: 2.588
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
-    Config: configs/unet-s5-d16/pspnet_unet_s5-d16_256x256_40k_hrf.py
-
-
-
-  - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
-    In Collection: DeepLabV3
-    Metadata:
-      inference time (ms/im):
-        - value: None
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: HRF
-        Metrics:
-          mIoU: 2.604
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
-    Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_256x256_40k_hrf.py

configs/unet/unet.yml

@@ -0,0 +1,177 @@
+Collections:
+- Name: unet
+  Metadata:
+    Training Data:
+    - DRIVE
+    - STARE
+    - CHASE_DB1
+    - HRF
+Models:
+- Name: fcn_unet_s5-d16_64x64_40k_drive
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (64,64)
+    lr schd: 40000
+    memory (GB): 0.68
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mIoU: 78.67
+  Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
+- Name: pspnet_unet_s5-d16_64x64_40k_drive
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (64,64)
+    lr schd: 40000
+    memory (GB): 0.599
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mIoU: 78.62
+  Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
+- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (64,64)
+    lr schd: 40000
+    memory (GB): 0.596
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mIoU: 78.69
+  Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
+- Name: fcn_unet_s5-d16_128x128_40k_stare
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (128,128)
+    lr schd: 40000
+    memory (GB): 0.968
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mIoU: 81.02
+  Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
+- Name: pspnet_unet_s5-d16_128x128_40k_stare
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (128,128)
+    lr schd: 40000
+    memory (GB): 0.982
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mIoU: 81.22
+  Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
+- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (128,128)
+    lr schd: 40000
+    memory (GB): 0.999
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mIoU: 80.93
+  Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
+- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (128,128)
+    lr schd: 40000
+    memory (GB): 0.968
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mIoU: 80.24
+  Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
+- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (128,128)
+    lr schd: 40000
+    memory (GB): 0.982
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mIoU: 80.36
+  Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
+- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (128,128)
+    lr schd: 40000
+    memory (GB): 0.999
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mIoU: 80.47
+  Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
+- Name: fcn_unet_s5-d16_256x256_40k_hrf
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (256,256)
+    lr schd: 40000
+    memory (GB): 2.525
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mIoU: 79.45
+  Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
+- Name: pspnet_unet_s5-d16_256x256_40k_hrf
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (256,256)
+    lr schd: 40000
+    memory (GB): 2.588
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mIoU: 80.07
+  Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
+- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
+  In Collection: unet
+  Metadata:
+    backbone: UNet-S5-D16
+    crop size: (256,256)
+    lr schd: 40000
+    memory (GB): 2.604
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mIoU: 80.21
+  Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth

configs/upernet/metafile.yml

@@ -1,311 +0,0 @@
-Collections:
-  - Name: UPerNet
-    Metadata:
-      Training Data:
-        - Cityscapes
-        - Pascal VOC 2012 + Aug
-        - ADE20K
-
-Models:
-
-  - Name: upernet_r50_512x1024_40k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 235.29
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
-    Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
-
-
-
-  - Name: upernet_r101_512x1024_40k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 263.85
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.69
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
-    Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
-
-
-
-  - Name: upernet_r50_769x769_40k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 568.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 77.98
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
-    Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
-
-
-
-  - Name: upernet_r101_769x769_40k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 641.03
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.03
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
-    Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
-
-
-
-  - Name: upernet_r50_512x1024_80k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 235.29
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 78.19
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
-    Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
-
-
-
-  - Name: upernet_r101_512x1024_80k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 263.85
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.40
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
-    Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
-
-
-
-  - Name: upernet_r50_769x769_80k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 568.18
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 79.39
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
-    Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
-
-
-
-  - Name: upernet_r101_769x769_80k_cityscapes
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 641.03
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Cityscapes
-        Metrics:
-          mIoU: 80.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
-    Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
-
-
-
-  - Name: upernet_r50_512x512_80k_ade20k
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 40.70
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
-    Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
-
-
-
-  - Name: upernet_r101_512x512_80k_ade20k
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 49.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.91
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
-    Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
-
-
-
-  - Name: upernet_r50_512x512_160k_ade20k
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 42.74
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 42.05
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
-    Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
-
-
-
-  - Name: upernet_r101_512x512_160k_ade20k
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 49.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: ADE20K
-        Metrics:
-          mIoU: 43.82
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
-    Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
-
-
-
-  - Name: upernet_r50_512x512_20k_voc12aug
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 43.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 74.82
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
-    Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
-
-
-
-  - Name: upernet_r101_512x512_20k_voc12aug
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 50.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.10
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
-    Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
-
-
-
-  - Name: upernet_r50_512x512_40k_voc12aug
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 43.16
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 75.92
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
-    Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
-
-
-
-  - Name: upernet_r101_512x512_40k_voc12aug
-    In Collection: UPerNet
-    Metadata:
-      inference time (ms/im):
-        - value: 50.05
-          hardware: V100
-          backend: PyTorch
-          batch size: 1
-          mode: FP32
-    Results:
-      - Task: Semantic Segmentation
-        Dataset: Pascal VOC 2012 + Aug
-        Metrics:
-          mIoU: 77.43
-    Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
-    Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py

configs/upernet/upernet.yml

@@ -0,0 +1,296 @@
+Collections:
+- Name: upernet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+Models:
+- Name: upernet_r50_512x1024_40k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 235.29
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 6.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.1
+      mIoU(ms+flip): 78.37
+  Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
+- Name: upernet_r101_512x1024_40k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 263.85
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 7.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.69
+      mIoU(ms+flip): 80.11
+  Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
+- Name: upernet_r50_769x769_40k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 568.18
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 7.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.98
+      mIoU(ms+flip): 79.7
+  Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
+- Name: upernet_r101_769x769_40k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 641.03
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    memory (GB): 8.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.03
+      mIoU(ms+flip): 80.77
+  Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
+- Name: upernet_r50_512x1024_80k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.19
+      mIoU(ms+flip): 79.19
+  Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
+- Name: upernet_r101_512x1024_80k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.4
+      mIoU(ms+flip): 80.46
+  Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
+- Name: upernet_r50_769x769_80k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.39
+      mIoU(ms+flip): 80.92
+  Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
+- Name: upernet_r101_769x769_80k_cityscapes
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.1
+      mIoU(ms+flip): 81.49
+  Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
+- Name: upernet_r50_512x512_80k_ade20k
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 42.74
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 8.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.7
+      mIoU(ms+flip): 41.81
+  Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
+- Name: upernet_r101_512x512_80k_ade20k
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 49.16
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.91
+      mIoU(ms+flip): 43.96
+  Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
+- Name: upernet_r50_512x512_160k_ade20k
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.05
+      mIoU(ms+flip): 42.78
+  Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
+- Name: upernet_r101_512x512_160k_ade20k
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.82
+      mIoU(ms+flip): 44.85
+  Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
+- Name: upernet_r50_512x512_20k_voc12aug
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 43.16
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 6.4
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.82
+      mIoU(ms+flip): 76.35
+  Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
+- Name: upernet_r101_512x512_20k_voc12aug
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 50.05
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.5
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.1
+      mIoU(ms+flip): 78.29
+  Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
+- Name: upernet_r50_512x512_40k_voc12aug
+  In Collection: upernet
+  Metadata:
+    backbone: R-50
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.92
+      mIoU(ms+flip): 77.44
+  Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
+- Name: upernet_r101_512x512_40k_voc12aug
+  In Collection: upernet
+  Metadata:
+    backbone: R-101
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.43
+      mIoU(ms+flip): 78.56
+  Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth

configs/vit/vit.yml

@@ -0,0 +1,248 @@
+Collections:
+- Name: vit
+  Metadata:
+    Training Data:
+    - ADE20K
+Models:
+- Name: upernet_vit-b16_mln_512x512_80k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: ViT-B + MLN
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 144.09
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.71
+      mIoU(ms+flip): 49.51
+  Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k-0403cee1.pth
+- Name: upernet_vit-b16_mln_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: ViT-B + MLN
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 131.93
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.75
+      mIoU(ms+flip): 48.46
+  Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k-852fa768.pth
+- Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: ViT-B + LN + MLN
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 146.63
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.21
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.73
+      mIoU(ms+flip): 49.95
+  Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k-f444c077.pth
+- Name: upernet_deit-s16_512x512_80k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-S
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 33.5
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 4.68
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.96
+      mIoU(ms+flip): 43.79
+  Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k-afc93ec2.pth
+- Name: upernet_deit-s16_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-S
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 34.26
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 4.68
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.87
+      mIoU(ms+flip): 43.79
+  Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k-5110d916.pth
+- Name: upernet_deit-s16_mln_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-S + MLN
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 89.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.69
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.82
+      mIoU(ms+flip): 45.07
+  Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k-fb9a5dfb.pth
+- Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-S + LN + MLN
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 80.71
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 5.69
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.52
+      mIoU(ms+flip): 45.01
+  Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k-c0cd652f.pth
+- Name: upernet_deit-b16_512x512_80k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-B
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 103.2
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.75
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.24
+      mIoU(ms+flip): 46.73
+  Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k-1e090789.pth
+- Name: upernet_deit-b16_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-B
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 96.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 7.75
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.36
+      mIoU(ms+flip): 47.16
+  Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k-828705d7.pth
+- Name: upernet_deit-b16_mln_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-B + MLN
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 128.53
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.21
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.46
+      mIoU(ms+flip): 47.16
+  Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k-4e1450f3.pth
+- Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
+  In Collection: vit
+  Metadata:
+    backbone: DeiT-B + LN + MLN
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 129.03
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    memory (GB): 9.21
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.37
+      mIoU(ms+flip): 47.23
+  Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k-8a959c14.pth

model-index.yml

@@ -1,27 +1,31 @@
 Import:
-  - configs/ann/metafile.yml
-  - configs/apcnet/metafile.yml
-  - configs/ccnet/metafile.yml
-  - configs/cgnet/metafile.yml
-  - configs/danet/metafile.yml
-  - configs/deeplabv3/metafile.yml
-  - configs/deeplabv3plus/metafile.yml
-  - configs/dnlnet/metafile.yml
-  - configs/emanet/metafile.yml
-  - configs/encnet/metafile.yml
-  - configs/fastscnn/metafile.yml
-  - configs/fcn/metafile.yml
-  - configs/fp16/metafile.yml
-  - configs/gcnet/metafile.yml
-  - configs/hrnet/metafile.yml
-  - configs/mobilenet_v2/metafile.yml
-  - configs/mobilenet_v3/metafile.yml
-  - configs/nonlocal_net/metafile.yml
-  - configs/ocrnet/metafile.yml
-  - configs/point_rend/metafile.yml
-  - configs/psanet/metafile.yml
-  - configs/pspnet/metafile.yml
-  - configs/resnest/metafile.yml
-  - configs/sem_fpn/metafile.yml
-  - configs/unet/metafile.yml
-  - configs/upernet/metafile.yml
+- configs/ann/ann.yml
+- configs/apcnet/apcnet.yml
+- configs/ccnet/ccnet.yml
+- configs/cgnet/cgnet.yml
+- configs/danet/danet.yml
+- configs/deeplabv3/deeplabv3.yml
+- configs/deeplabv3plus/deeplabv3plus.yml
+- configs/dmnet/dmnet.yml
+- configs/dnlnet/dnlnet.yml
+- configs/emanet/emanet.yml
+- configs/encnet/encnet.yml
+- configs/fastscnn/fastscnn.yml
+- configs/fcn/fcn.yml
+- configs/fp16/fp16.yml
+- configs/gcnet/gcnet.yml
+- configs/hrnet/hrnet.yml
+- configs/mobilenet_v2/mobilenet_v2.yml
+- configs/mobilenet_v3/mobilenet_v3.yml
+- configs/nonlocal_net/nonlocal_net.yml
+- configs/ocrnet/ocrnet.yml
+- configs/point_rend/point_rend.yml
+- configs/psanet/psanet.yml
+- configs/pspnet/pspnet.yml
+- configs/resnest/resnest.yml
+- configs/sem_fpn/sem_fpn.yml
+- configs/setr/setr.yml
+- configs/swin/swin.yml
+- configs/unet/unet.yml
+- configs/upernet/upernet.yml
+- configs/vit/vit.yml