charlie880624/STDC
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix random behavior of update_model_index in pre-commit hook (#784)

Browse Source
This commit is contained in:
Junjun2016 2021-08-15 23:33:08 +08:00 committed by GitHub
parent 2acd563231
commit 6eff94165c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
32 changed files with 2258 additions and 2251 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
.dev/md2yml.py
View File

@ -25,17 +25,23 @@ def dump_yaml_and_check_difference(obj, filename):
Returns:
Bool: If the target YAML file is different from the original.
"""
original = None
str_dump = mmcv.dump(obj, None, file_format='yaml', sort_keys=True)
if osp.isfile(filename):
file_exists = True
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)
str_orig = f.read()
else:
file_exists = False
str_orig = None
if file_exists and str_orig == str_dump:
is_different = False
else:
is_different = True
with open(filename, 'w', encoding='utf-8') as f:
f.write(str_dump)
return is_different
@ -183,11 +189,11 @@ def update_model_index():
if __name__ == '__main__':
file_list = [fn for fn in sys.argv[1:] if osp.basename(fn) == 'README.md']
if not file_list:
exit(0)
sys.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)
sys.exit(1 if file_modified else 0)

1
.pre-commit-config.yaml
View File

@ -47,3 +47,4 @@ repos:
additional_dependencies: [mmcv]
language: python
files: ^configs/.*\.md$
require_serial: true

164
configs/ann/ann.yml
View File

@ -1,296 +1,296 @@
Collections:
- Name: ann
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: ann
Models:
- Name: ann_r50-d8_512x1024_40k_cityscapes
- Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 269.54
lr schd: 40000
memory (GB): 6.0
Name: ann_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 392.16
lr schd: 40000
memory (GB): 9.5
Name: ann_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 588.24
lr schd: 40000
memory (GB): 6.8
Name: ann_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 869.57
lr schd: 40000
memory (GB): 10.7
Name: ann_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
In Collection: ann
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: ann_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
In Collection: ann
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: ann_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
In Collection: ann
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: ann_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
In Collection: ann
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: ann_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.6
lr schd: 80000
memory (GB): 9.1
Name: ann_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 70.82
lr schd: 80000
memory (GB): 12.5
Name: ann_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
In Collection: ann
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: ann_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
In Collection: ann
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: ann_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.8
lr schd: 20000
memory (GB): 6.0
Name: ann_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 71.74
lr schd: 20000
memory (GB): 9.5
Name: ann_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
In Collection: ann
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: ann_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py
In Collection: ann
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: ann_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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

124
configs/apcnet/apcnet.yml
View File

@ -1,223 +1,223 @@
Collections:
- Name: apcnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: apcnet
Models:
- Name: apcnet_r50-d8_512x1024_40k_cityscapes
- Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 280.11
lr schd: 40000
memory (GB): 7.7
Name: apcnet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 465.12
lr schd: 40000
memory (GB): 11.2
Name: apcnet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 657.89
lr schd: 40000
memory (GB): 8.7
Name: apcnet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 970.87
lr schd: 40000
memory (GB): 12.7
Name: apcnet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: apcnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: apcnet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: apcnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: apcnet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
In Collection: apcnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: apcnet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
In Collection: apcnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: apcnet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 50.99
lr schd: 80000
memory (GB): 10.1
Name: apcnet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 76.34
lr schd: 80000
memory (GB): 13.6
Name: apcnet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
In Collection: apcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: apcnet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py
In Collection: apcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: apcnet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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

164
configs/ccnet/ccnet.yml
View File

@ -1,296 +1,296 @@
Collections:
- Name: ccnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: ccnet
Models:
- Name: ccnet_r50-d8_512x1024_40k_cityscapes
- Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 301.2
lr schd: 40000
memory (GB): 6.0
Name: ccnet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 432.9
lr schd: 40000
memory (GB): 9.5
Name: ccnet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 699.3
lr schd: 40000
memory (GB): 6.8
Name: ccnet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 990.1
lr schd: 40000
memory (GB): 10.7
Name: ccnet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: ccnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: ccnet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: ccnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: ccnet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
In Collection: ccnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: ccnet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
In Collection: ccnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: ccnet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.87
lr schd: 80000
memory (GB): 8.8
Name: ccnet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 70.87
lr schd: 80000
memory (GB): 12.2
Name: ccnet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
In Collection: ccnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: ccnet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
In Collection: ccnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: ccnet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 48.9
lr schd: 20000
memory (GB): 6.0
Name: ccnet_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 73.31
lr schd: 20000
memory (GB): 9.5
Name: ccnet_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
In Collection: ccnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: ccnet_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
In Collection: ccnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: ccnet_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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

32
configs/cgnet/cgnet.yml
View File

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

164
configs/danet/danet.yml
View File

@ -1,292 +1,292 @@
Collections:
- Name: danet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: danet
Models:
- Name: danet_r50-d8_512x1024_40k_cityscapes
- Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 375.94
lr schd: 40000
memory (GB): 7.4
Name: danet_r50-d8_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.74
Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 502.51
lr schd: 40000
memory (GB): 10.9
Name: danet_r101-d8_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.52
Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 641.03
lr schd: 40000
memory (GB): 8.8
Name: danet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 934.58
lr schd: 40000
memory (GB): 12.8
Name: danet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: danet_r50-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.34
Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: danet_r101-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: danet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: danet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.17
lr schd: 80000
memory (GB): 11.5
Name: danet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 70.52
lr schd: 80000
memory (GB): 15.0
Name: danet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: danet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: danet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.76
lr schd: 20000
memory (GB): 6.5
Name: danet_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 72.67
lr schd: 20000
memory (GB): 9.9
Name: danet_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: danet_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: danet_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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

314
configs/deeplabv3/deeplabv3.yml
View File

@ -1,552 +1,552 @@
Collections:
- Name: deeplabv3
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: deeplabv3
Models:
- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 389.11
lr schd: 40000
memory (GB): 6.1
Name: deeplabv3_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 520.83
lr schd: 40000
memory (GB): 9.6
Name: deeplabv3_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 900.9
lr schd: 40000
memory (GB): 6.9
Name: deeplabv3_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 1204.82
lr schd: 40000
memory (GB): 10.9
Name: deeplabv3_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 72.57
lr schd: 80000
memory (GB): 1.7
Name: deeplabv3_r18-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 180.18
lr schd: 80000
memory (GB): 1.9
Name: deeplabv3_r18-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: deeplabv3_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: deeplabv3_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 71.79
lr schd: 80000
memory (GB): 1.6
Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 364.96
lr schd: 80000
memory (GB): 6.0
Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 552.49
lr schd: 80000
memory (GB): 9.5
Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 172.71
lr schd: 80000
memory (GB): 1.8
Name: deeplabv3_r18b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 862.07
lr schd: 80000
memory (GB): 6.8
Name: deeplabv3_r50b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 1219.51
lr schd: 80000
memory (GB): 10.7
Name: deeplabv3_r101b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 67.75
lr schd: 80000
memory (GB): 8.9
Name: deeplabv3_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 98.62
lr schd: 80000
memory (GB): 12.4
Name: deeplabv3_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: deeplabv3_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: deeplabv3_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 72.05
lr schd: 20000
memory (GB): 6.1
Name: deeplabv3_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 101.94
lr schd: 20000
memory (GB): 9.6
Name: deeplabv3_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py
In Collection: deeplabv3
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: deeplabv3_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: deeplabv3_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 141.04
lr schd: 40000
memory (GB): 9.2
Name: deeplabv3_r101-d8_480x480_40k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: deeplabv3_r101-d8_480x480_80k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py
In Collection: deeplabv3
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59
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
Task: Semantic Segmentation
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

328
configs/deeplabv3plus/deeplabv3plus.yml
View File

@ -1,574 +1,574 @@
Collections:
- Name: deeplabv3plus
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- ' Pascal VOC 2012 + Aug'
- ' Pascal Context'
- ' Pascal Context 59'
Name: deeplabv3plus
Models:
- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 253.81
lr schd: 40000
memory (GB): 7.5
Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 384.62
lr schd: 40000
memory (GB): 11.0
Name: deeplabv3plus_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 581.4
lr schd: 40000
memory (GB): 8.5
Name: deeplabv3plus_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 869.57
lr schd: 40000
memory (GB): 12.5
Name: deeplabv3plus_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 70.08
lr schd: 80000
memory (GB): 2.2
Name: deeplabv3plus_r18-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3plus
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3plus_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: deeplabv3plus_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 174.22
lr schd: 80000
memory (GB): 2.5
Name: deeplabv3plus_r18-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3plus
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: deeplabv3plus_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: deeplabv3plus_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 133.69
lr schd: 40000
memory (GB): 5.8
Name: deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D16-MG124
crop size: (512,1024)
lr schd: 80000
memory (GB): 9.9
Name: deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 66.89
lr schd: 80000
memory (GB): 2.1
Name: deeplabv3plus_r18b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 253.81
lr schd: 80000
memory (GB): 7.4
Name: deeplabv3plus_r50b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 384.62
lr schd: 80000
memory (GB): 10.9
Name: deeplabv3plus_r101b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 167.79
lr schd: 80000
memory (GB): 2.4
Name: deeplabv3plus_r18b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 581.4
lr schd: 80000
memory (GB): 8.4
Name: deeplabv3plus_r50b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 909.09
lr schd: 80000
memory (GB): 12.3
Name: deeplabv3plus_r101b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.6
lr schd: 80000
memory (GB): 10.6
Name: deeplabv3plus_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 70.62
lr schd: 80000
memory (GB): 14.1
Name: deeplabv3plus_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py
In Collection: deeplabv3plus
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: deeplabv3plus_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: deeplabv3plus_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.62
lr schd: 20000
memory (GB): 7.6
Name: deeplabv3plus_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 72.05
lr schd: 20000
memory (GB): 11.0
Name: deeplabv3plus_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py
In Collection: deeplabv3plus
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: deeplabv3plus_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: deeplabv3plus_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 110.01
lr schd: 40000
Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Name: deeplabv3plus_r101-d8_480x480_40k_pascal_context_59
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
Task: Semantic Segmentation
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
- Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
In Collection: deeplabv3plus
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: deeplabv3plus_r101-d8_480x480_80k_pascal_context_59
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
Task: Semantic Segmentation
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

124
configs/dmnet/dmnet.yml
View File

@ -1,223 +1,223 @@
Collections:
- Name: dmnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: dmnet
Models:
- Name: dmnet_r50-d8_512x1024_40k_cityscapes
- Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 273.22
lr schd: 40000
memory (GB): 7.0
Name: dmnet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 393.7
lr schd: 40000
memory (GB): 10.6
Name: dmnet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 636.94
lr schd: 40000
memory (GB): 7.9
Name: dmnet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 990.1
lr schd: 40000
memory (GB): 12.0
Name: dmnet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: dmnet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: dmnet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: dmnet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: dmnet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.73
lr schd: 80000
memory (GB): 9.4
Name: dmnet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 72.05
lr schd: 80000
memory (GB): 13.0
Name: dmnet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
In Collection: dmnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: dmnet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
In Collection: dmnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: dmnet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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

124
configs/dnlnet/dnlnet.yml
View File

@ -1,219 +1,219 @@
Collections:
- Name: dnlnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: dnlnet
Models:
- Name: dnl_r50-d8_512x1024_40k_cityscapes
- Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 390.62
lr schd: 40000
memory (GB): 7.3
Name: dnl_r50-d8_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.61
Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 510.2
lr schd: 40000
memory (GB): 10.9
Name: dnl_r101-d8_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.31
Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 666.67
lr schd: 40000
memory (GB): 9.2
Name: dnl_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 980.39
lr schd: 40000
memory (GB): 12.6
Name: dnl_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: dnl_r50-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.33
Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: dnl_r101-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: dnl_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
In Collection: dnlnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: dnl_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 48.4
lr schd: 80000
memory (GB): 8.8
Name: dnl_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 79.74
lr schd: 80000
memory (GB): 12.8
Name: dnl_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py
In Collection: dnlnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: dnl_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py
In Collection: dnlnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: dnl_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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

60
configs/emanet/emanet.yml
View File

@ -1,94 +1,94 @@
Collections:
- Name: emanet
Metadata:
- Metadata:
Training Data:
- Cityscapes
Name: emanet
Models:
- Name: emanet_r50-d8_512x1024_80k_cityscapes
- Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 218.34
lr schd: 80000
memory (GB): 5.4
Name: emanet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 348.43
lr schd: 80000
memory (GB): 6.2
Name: emanet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 507.61
lr schd: 80000
memory (GB): 8.9
Name: emanet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 819.67
lr schd: 80000
memory (GB): 10.1
Name: emanet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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

124
configs/encnet/encnet.yml
View File

@ -1,223 +1,223 @@
Collections:
- Name: encnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: encnet
Models:
- Name: encnet_r50-d8_512x1024_40k_cityscapes
- Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 218.34
lr schd: 40000
memory (GB): 8.6
Name: encnet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 375.94
lr schd: 40000
memory (GB): 12.1
Name: encnet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 549.45
lr schd: 40000
memory (GB): 9.8
Name: encnet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 793.65
lr schd: 40000
memory (GB): 13.7
Name: encnet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: encnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: encnet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: encnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: encnet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
In Collection: encnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: encnet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
In Collection: encnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: encnet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 43.84
lr schd: 80000
memory (GB): 10.1
Name: encnet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 67.25
lr schd: 80000
memory (GB): 13.6
Name: encnet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
In Collection: encnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: encnet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
In Collection: encnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: encnet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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

18
configs/fastscnn/fastscnn.yml
View File

@ -1,28 +1,28 @@
Collections:
- Name: fastscnn
Metadata:
- Metadata:
Training Data:
- Cityscapes
Name: fastscnn
Models:
- Name: fast_scnn_lr0.12_8x4_160k_cityscapes
- Config: configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 17.71
lr schd: 160000
memory (GB): 3.3
Name: fast_scnn_lr0.12_8x4_160k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.96
mIoU(ms+flip): 72.65
Config: configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_8x4_160k_lr0.12_cityscapes-0cec9937.pth

476
configs/fcn/fcn.yml
View File

File diff suppressed because it is too large Load Diff

60
configs/fp16/fp16.yml
View File

@ -1,90 +1,90 @@
Collections:
- Name: fp16
Metadata:
- Metadata:
Training Data:
- Cityscapes
Name: fp16
Models:
- Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
- Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 115.74
lr schd: 80000
memory (GB): 5.37
Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.8
Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 114.03
lr schd: 80000
memory (GB): 5.34
Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.46
Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 259.07
lr schd: 80000
memory (GB): 5.75
Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.48
Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 127.06
lr schd: 80000
memory (GB): 6.35
Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.46
Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
Task: Semantic Segmentation
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

164
configs/gcnet/gcnet.yml
View File

@ -1,296 +1,296 @@
Collections:
- Name: gcnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: gcnet
Models:
- Name: gcnet_r50-d8_512x1024_40k_cityscapes
- Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 254.45
lr schd: 40000
memory (GB): 5.8
Name: gcnet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 383.14
lr schd: 40000
memory (GB): 9.2
Name: gcnet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 598.8
lr schd: 40000
memory (GB): 6.5
Name: gcnet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 884.96
lr schd: 40000
memory (GB): 10.5
Name: gcnet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: gcnet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: gcnet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: gcnet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: gcnet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.77
lr schd: 80000
memory (GB): 8.5
Name: gcnet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 65.79
lr schd: 80000
memory (GB): 12.0
Name: gcnet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: gcnet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: gcnet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.83
lr schd: 20000
memory (GB): 5.8
Name: gcnet_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 67.57
lr schd: 20000
memory (GB): 9.2
Name: gcnet_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py
In Collection: gcnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: gcnet_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py
In Collection: gcnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: gcnet_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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

234
configs/hrnet/hrnet.yml
View File

@ -1,440 +1,440 @@
Collections:
- Name: hrnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: hrnet
Models:
- Name: fcn_hr18s_512x1024_40k_cityscapes
- Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 42.12
lr schd: 40000
memory (GB): 1.7
Name: fcn_hr18s_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.86
mIoU(ms+flip): 75.91
Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 77.1
lr schd: 40000
memory (GB): 2.9
Name: fcn_hr18_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.19
mIoU(ms+flip): 78.92
Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 155.76
lr schd: 40000
memory (GB): 6.2
Name: fcn_hr48_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.48
mIoU(ms+flip): 79.69
Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 80000
Name: fcn_hr18s_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.31
mIoU(ms+flip): 77.48
Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 80000
Name: fcn_hr18_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.65
mIoU(ms+flip): 80.35
Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 80000
Name: fcn_hr48_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.93
mIoU(ms+flip): 80.72
Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 160000
Name: fcn_hr18s_512x1024_160k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.31
mIoU(ms+flip): 78.31
Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 160000
Name: fcn_hr18_512x1024_160k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.74
Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 160000
Name: fcn_hr48_512x1024_160k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.65
mIoU(ms+flip): 81.92
Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 25.87
lr schd: 80000
memory (GB): 3.8
Name: fcn_hr18s_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 31.38
mIoU(ms+flip): 32.45
Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 44.31
lr schd: 80000
memory (GB): 4.9
Name: fcn_hr18_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 35.51
mIoU(ms+flip): 36.8
Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.1
lr schd: 80000
memory (GB): 8.2
Name: fcn_hr48_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.9
mIoU(ms+flip): 43.27
Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 160000
Name: fcn_hr18s_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 33.0
mIoU(ms+flip): 34.55
Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 160000
Name: fcn_hr18_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 36.79
mIoU(ms+flip): 38.58
Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 160000
Name: fcn_hr48_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.02
mIoU(ms+flip): 43.86
Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 23.06
lr schd: 20000
memory (GB): 1.8
Name: fcn_hr18s_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.59
lr schd: 20000
memory (GB): 2.9
Name: fcn_hr18_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 45.35
lr schd: 20000
memory (GB): 6.2
Name: fcn_hr48_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 40000
Name: fcn_hr18s_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 40000
Name: fcn_hr18_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 40000
Name: fcn_hr48_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 112.87
lr schd: 40000
memory (GB): 6.1
Name: fcn_hr48_480x480_40k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 80000
Name: fcn_hr48_480x480_80k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 40000
Name: fcn_hr48_480x480_40k_pascal_context_59
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
Task: Semantic Segmentation
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
- Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
lr schd: 80000
Name: fcn_hr48_480x480_80k_pascal_context_59
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
Task: Semantic Segmentation
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

116
configs/mobilenet_v2/mobilenet_v2.yml
View File

@ -1,175 +1,175 @@
Collections:
- Name: mobilenet_v2
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20k
Name: mobilenet_v2
Models:
- Name: fcn_m-v2-d8_512x1024_80k_cityscapes
- Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 70.42
lr schd: 80000
memory (GB): 3.4
Name: fcn_m-v2-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 61.54
Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 89.29
lr schd: 80000
memory (GB): 3.6
Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.23
Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 119.05
lr schd: 80000
memory (GB): 3.9
Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.84
Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 119.05
lr schd: 80000
memory (GB): 5.1
Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.2
Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 15.53
lr schd: 160000
memory (GB): 6.5
Name: fcn_m-v2-d8_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 19.71
Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 17.33
lr schd: 160000
memory (GB): 6.5
Name: pspnet_m-v2-d8_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 29.68
Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 25.06
lr schd: 160000
memory (GB): 6.8
Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 34.08
Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 23.2
lr schd: 160000
memory (GB): 8.2
Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20k
Metrics:
mIoU: 34.02
Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
Task: Semantic Segmentation
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

60
configs/mobilenet_v3/mobilenet_v3.yml
View File

@ -1,94 +1,94 @@
Collections:
- Name: mobilenet_v3
Metadata:
- Metadata:
Training Data:
- Cityscapes
Name: mobilenet_v3
Models:
- Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
- Config: configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 65.7
lr schd: 320000
memory (GB): 8.9
Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 67.7
lr schd: 320000
memory (GB): 8.9
Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 42.3
lr schd: 320000
memory (GB): 5.3
Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 40.82
lr schd: 320000
memory (GB): 5.3
Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
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
Task: Semantic Segmentation
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

164
configs/nonlocal_net/nonlocal_net.yml
View File

@ -1,292 +1,292 @@
Collections:
- Name: nonlocal_net
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: nonlocal_net
Models:
- Name: nonlocal_r50-d8_512x1024_40k_cityscapes
- Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 367.65
lr schd: 40000
memory (GB): 7.4
Name: nonlocal_r50-d8_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.24
Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 512.82
lr schd: 40000
memory (GB): 10.9
Name: nonlocal_r101-d8_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.66
Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 657.89
lr schd: 40000
memory (GB): 8.9
Name: nonlocal_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 952.38
lr schd: 40000
memory (GB): 12.8
Name: nonlocal_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
In Collection: nonlocal_net
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: nonlocal_r50-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.01
Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py
In Collection: nonlocal_net
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: nonlocal_r101-d8_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.93
Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py
In Collection: nonlocal_net
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: nonlocal_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py
In Collection: nonlocal_net
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: nonlocal_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 46.79
lr schd: 80000
memory (GB): 9.1
Name: nonlocal_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 71.58
lr schd: 80000
memory (GB): 12.6
Name: nonlocal_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py
In Collection: nonlocal_net
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: nonlocal_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py
In Collection: nonlocal_net
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: nonlocal_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.15
lr schd: 20000
memory (GB): 6.4
Name: nonlocal_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 71.38
lr schd: 20000
memory (GB): 9.8
Name: nonlocal_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py
In Collection: nonlocal_net
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: nonlocal_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py
In Collection: nonlocal_net
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: nonlocal_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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

236
configs/ocrnet/ocrnet.yml
View File

@ -1,431 +1,431 @@
Collections:
- Name: ocrnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ' HRNet backbone'
- ' ResNet backbone'
- ADE20K
- Pascal VOC 2012 + Aug
Name: ocrnet
Models:
- Name: ocrnet_hr18s_512x1024_40k_cityscapes
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 95.69
lr schd: 40000
memory (GB): 3.5
Name: ocrnet_hr18s_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 133.33
lr schd: 40000
memory (GB): 4.7
Name: ocrnet_hr18_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 236.97
lr schd: 40000
memory (GB): 8.0
Name: ocrnet_hr48_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 80000
Name: ocrnet_hr18s_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 80000
Name: ocrnet_hr18_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 80000
Name: ocrnet_hr48_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 160000
Name: ocrnet_hr18s_512x1024_160k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 160000
Name: ocrnet_hr18_512x1024_160k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 160000
Name: ocrnet_hr48_512x1024_160k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
Results:
Task: Semantic Segmentation
Dataset: ' ResNet backbone'
Metrics:
mIoU: 80.09
Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 331.13
lr schd: 40000
memory (GB): 8.8
Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
Results:
Task: Semantic Segmentation
Dataset: ' ResNet backbone'
Metrics:
mIoU: 80.3
Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 331.13
lr schd: 80000
memory (GB): 8.8
Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
Results:
Task: Semantic Segmentation
Dataset: ' ResNet backbone'
Metrics:
mIoU: 80.81
Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 34.51
lr schd: 80000
memory (GB): 6.7
Name: ocrnet_hr18s_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 35.06
mIoU(ms+flip): 35.8
Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 52.83
lr schd: 80000
memory (GB): 7.9
Name: ocrnet_hr18_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.79
mIoU(ms+flip): 39.16
Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 58.86
lr schd: 80000
memory (GB): 11.2
Name: ocrnet_hr48_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.0
mIoU(ms+flip): 44.3
Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 160000
Name: ocrnet_hr18s_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.19
mIoU(ms+flip): 38.4
Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 160000
Name: ocrnet_hr18_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.32
mIoU(ms+flip): 40.8
Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 160000
Name: ocrnet_hr48_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.25
mIoU(ms+flip): 44.88
Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 31.7
lr schd: 20000
memory (GB): 3.5
Name: ocrnet_hr18s_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 50.23
lr schd: 20000
memory (GB): 4.7
Name: ocrnet_hr18_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 56.09
lr schd: 20000
memory (GB): 8.1
Name: ocrnet_hr48_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 40000
Name: ocrnet_hr18s_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 40000
Name: ocrnet_hr18_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 40000
Name: ocrnet_hr48_512x512_40k_voc12aug
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
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth

60
configs/point_rend/point_rend.yml
View File

@ -1,95 +1,95 @@
Collections:
- Name: point_rend
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: point_rend
Models:
- Name: pointrend_r50_512x1024_80k_cityscapes
- Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 117.92
lr schd: 80000
memory (GB): 3.1
Name: pointrend_r50_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 142.86
lr schd: 80000
memory (GB): 4.2
Name: pointrend_r101_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 57.77
lr schd: 160000
memory (GB): 5.1
Name: pointrend_r50_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 64.52
lr schd: 160000
memory (GB): 6.1
Name: pointrend_r101_512x512_160k_ade20k
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
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth

164
configs/psanet/psanet.yml
View File

@ -1,296 +1,296 @@
Collections:
- Name: psanet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: psanet
Models:
- Name: psanet_r50-d8_512x1024_40k_cityscapes
- Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 315.46
lr schd: 40000
memory (GB): 7.0
Name: psanet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 454.55
lr schd: 40000
memory (GB): 10.5
Name: psanet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 714.29
lr schd: 40000
memory (GB): 7.9
Name: psanet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 1020.41
lr schd: 40000
memory (GB): 11.9
Name: psanet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: psanet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: psanet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: psanet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: psanet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 52.88
lr schd: 80000
memory (GB): 9.0
Name: psanet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 76.16
lr schd: 80000
memory (GB): 12.5
Name: psanet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: psanet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: psanet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 54.82
lr schd: 20000
memory (GB): 6.9
Name: psanet_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 79.18
lr schd: 20000
memory (GB): 10.4
Name: psanet_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py
In Collection: psanet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: psanet_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py
In Collection: psanet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: psanet_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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

308
configs/pspnet/pspnet.yml
View File

@ -1,538 +1,538 @@
Collections:
- Name: pspnet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: pspnet
Models:
- Name: pspnet_r50-d8_512x1024_40k_cityscapes
- Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 245.7
lr schd: 40000
memory (GB): 6.1
Name: pspnet_r50-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 373.13
lr schd: 40000
memory (GB): 9.6
Name: pspnet_r101-d8_512x1024_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 568.18
lr schd: 40000
memory (GB): 6.9
Name: pspnet_r50-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 869.57
lr schd: 40000
memory (GB): 10.9
Name: pspnet_r101-d8_769x769_40k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 63.65
lr schd: 80000
memory (GB): 1.7
Name: pspnet_r18-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: pspnet_r50-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: pspnet_r101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 161.29
lr schd: 80000
memory (GB): 1.9
Name: pspnet_r18-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: pspnet_r50-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: pspnet_r101-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 61.43
lr schd: 80000
memory (GB): 1.5
Name: pspnet_r18b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 232.56
lr schd: 80000
memory (GB): 6.0
Name: pspnet_r50b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 362.32
lr schd: 80000
memory (GB): 9.5
Name: pspnet_r101b-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 156.01
lr schd: 80000
memory (GB): 1.7
Name: pspnet_r18b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 531.91
lr schd: 80000
memory (GB): 6.8
Name: pspnet_r50b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 854.7
lr schd: 80000
memory (GB): 10.8
Name: pspnet_r101b-d8_769x769_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.5
lr schd: 80000
memory (GB): 8.5
Name: pspnet_r50-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 65.36
lr schd: 80000
memory (GB): 12.0
Name: pspnet_r101-d8_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: pspnet_r50-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: pspnet_r101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.39
lr schd: 20000
memory (GB): 6.1
Name: pspnet_r50-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 66.58
lr schd: 20000
memory (GB): 9.6
Name: pspnet_r101-d8_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py
In Collection: pspnet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: pspnet_r50-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: pspnet_r101-d8_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (480,480)
value: 103.31
lr schd: 40000
memory (GB): 8.8
Name: pspnet_r101-d8_480x480_40k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: pspnet_r101-d8_480x480_80k_pascal_context
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 40000
Name: pspnet_r101-d8_480x480_40k_pascal_context_59
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
Task: Semantic Segmentation
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
- Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py
In Collection: pspnet
Metadata:
backbone: R-101-D8
crop size: (480,480)
lr schd: 80000
Name: pspnet_r101-d8_480x480_80k_pascal_context_59
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
Task: Semantic Segmentation
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

116
configs/resnest/resnest.yml
View File

@ -1,183 +1,183 @@
Collections:
- Name: resnest
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20k
Name: resnest
Models:
- Name: fcn_s101-d8_512x1024_80k_cityscapes
- Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 418.41
lr schd: 80000
memory (GB): 11.4
Name: fcn_s101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 396.83
lr schd: 80000
memory (GB): 11.8
Name: pspnet_s101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 531.91
lr schd: 80000
memory (GB): 11.9
Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 423.73
lr schd: 80000
memory (GB): 13.2
Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 77.76
lr schd: 160000
memory (GB): 14.2
Name: fcn_s101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 76.8
lr schd: 160000
memory (GB): 14.2
Name: pspnet_s101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 107.76
lr schd: 160000
memory (GB): 14.6
Name: deeplabv3_s101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 83.61
lr schd: 160000
memory (GB): 16.2
Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
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
Task: Semantic Segmentation
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

60
configs/sem_fpn/sem_fpn.yml
View File

@ -1,95 +1,95 @@
Collections:
- Name: sem_fpn
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
Name: sem_fpn
Models:
- Name: fpn_r50_512x1024_80k_cityscapes
- Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 73.86
lr schd: 80000
memory (GB): 2.8
Name: fpn_r50_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 97.18
lr schd: 80000
memory (GB): 3.9
Name: fpn_r101_512x1024_80k_cityscapes
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
Task: Semantic Segmentation
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
- Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 17.93
lr schd: 160000
memory (GB): 4.9
Name: fpn_r50_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 24.64
lr schd: 160000
memory (GB): 5.9
Name: fpn_r101_512x512_160k_ade20k
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
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth

52
configs/setr/setr.yml
View File

@ -1,87 +1,87 @@
Collections:
- Name: setr
Metadata:
- Metadata:
Training Data:
- ADE20K
Name: setr
Models:
- Name: setr_naive_512x512_160k_b16_ade20k
- Config: configs/setr/setr_naive_512x512_160k_b16_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 211.86
lr schd: 160000
memory (GB): 18.4
Name: setr_naive_512x512_160k_b16_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/setr/setr_pup_512x512_160k_b16_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 222.22
lr schd: 160000
memory (GB): 19.54
Name: setr_pup_512x512_160k_b16_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/setr/setr_mla_512x512_160k_b8_ade20k.py
In Collection: setr
Metadata:
backbone: ViT-L
crop size: (512,512)
lr schd: 160000
memory (GB): 10.96
Name: setr_mla_512x512_160k_b8_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/setr/setr_mla_512x512_160k_b16_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 190.48
lr schd: 160000
memory (GB): 17.3
Name: setr_mla_512x512_160k_b16_ade20k
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
Task: Semantic Segmentation
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

72
configs/swin/swin.yml
View File

@ -1,122 +1,122 @@
Collections:
- Name: swin
Metadata:
- Metadata:
Training Data:
- ADE20K
Name: swin
Models:
- Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
- Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.48
lr schd: 160000
memory (GB): 5.02
Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
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
Task: Semantic Segmentation
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
- Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 67.93
lr schd: 160000
memory (GB): 6.17
Name: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
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
Task: Semantic Segmentation
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
- Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 79.05
lr schd: 160000
memory (GB): 7.61
Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K
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
Task: Semantic Segmentation
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
- Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py
In Collection: swin
Metadata:
backbone: Swin-B
crop size: (512,512)
lr schd: 160000
Name: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K
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
Task: Semantic Segmentation
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
- Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 82.64
lr schd: 160000
memory (GB): 8.52
Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K
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
Task: Semantic Segmentation
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
- Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py
In Collection: swin
Metadata:
backbone: Swin-B
crop size: (512,512)
lr schd: 160000
Name: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K
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
Task: Semantic Segmentation
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

76
configs/unet/unet.yml
View File

@ -1,177 +1,177 @@
Collections:
- Name: unet
Metadata:
- Metadata:
Training Data:
- DRIVE
- STARE
- CHASE_DB1
- HRF
Name: unet
Models:
- Name: fcn_unet_s5-d16_64x64_40k_drive
- Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.68
Name: fcn_unet_s5-d16_64x64_40k_drive
Results:
Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 78.67
Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.599
Name: pspnet_unet_s5-d16_64x64_40k_drive
Results:
Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 78.62
Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.596
Name: deeplabv3_unet_s5-d16_64x64_40k_drive
Results:
Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 78.69
Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
Task: Semantic Segmentation
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
- Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Name: fcn_unet_s5-d16_128x128_40k_stare
Results:
Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 81.02
Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Name: pspnet_unet_s5-d16_128x128_40k_stare
Results:
Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 81.22
Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Name: deeplabv3_unet_s5-d16_128x128_40k_stare
Results:
Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 80.93
Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
Task: Semantic Segmentation
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
- Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Name: fcn_unet_s5-d16_128x128_40k_chase_db1
Results:
Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 80.24
Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
Results:
Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 80.36
Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
Results:
Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 80.47
Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
Task: Semantic Segmentation
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
- Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.525
Name: fcn_unet_s5-d16_256x256_40k_hrf
Results:
Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 79.45
Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.588
Name: pspnet_unet_s5-d16_256x256_40k_hrf
Results:
Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 80.07
Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.604
Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
Results:
Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 80.21
Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
Task: Semantic Segmentation
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

164
configs/upernet/upernet.yml
View File

@ -1,296 +1,296 @@
Collections:
- Name: upernet
Metadata:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: upernet
Models:
- Name: upernet_r50_512x1024_40k_cityscapes
- Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 235.29
lr schd: 40000
memory (GB): 6.4
Name: upernet_r50_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.1
mIoU(ms+flip): 78.37
Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 263.85
lr schd: 40000
memory (GB): 7.4
Name: upernet_r101_512x1024_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.69
mIoU(ms+flip): 80.11
Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 568.18
lr schd: 40000
memory (GB): 7.2
Name: upernet_r50_769x769_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.98
mIoU(ms+flip): 79.7
Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 641.03
lr schd: 40000
memory (GB): 8.4
Name: upernet_r101_769x769_40k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
mIoU(ms+flip): 80.77
Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
In Collection: upernet
Metadata:
backbone: R-50
crop size: (512,1024)
lr schd: 80000
Name: upernet_r50_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.19
mIoU(ms+flip): 79.19
Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
In Collection: upernet
Metadata:
backbone: R-101
crop size: (512,1024)
lr schd: 80000
Name: upernet_r101_512x1024_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.4
mIoU(ms+flip): 80.46
Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
In Collection: upernet
Metadata:
backbone: R-50
crop size: (769,769)
lr schd: 80000
Name: upernet_r50_769x769_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.39
mIoU(ms+flip): 80.92
Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
In Collection: upernet
Metadata:
backbone: R-101
crop size: (769,769)
lr schd: 80000
Name: upernet_r101_769x769_80k_cityscapes
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.1
mIoU(ms+flip): 81.49
Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 42.74
lr schd: 80000
memory (GB): 8.1
Name: upernet_r50_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.7
mIoU(ms+flip): 41.81
Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 49.16
lr schd: 80000
memory (GB): 9.1
Name: upernet_r101_512x512_80k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.91
mIoU(ms+flip): 43.96
Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
In Collection: upernet
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 160000
Name: upernet_r50_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.05
mIoU(ms+flip): 42.78
Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
In Collection: upernet
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 160000
Name: upernet_r101_512x512_160k_ade20k
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.82
mIoU(ms+flip): 44.85
Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 43.16
lr schd: 20000
memory (GB): 6.4
Name: upernet_r50_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 50.05
lr schd: 20000
memory (GB): 7.5
Name: upernet_r101_512x512_20k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
In Collection: upernet
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 40000
Name: upernet_r50_512x512_40k_voc12aug
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
Task: Semantic Segmentation
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
- Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
In Collection: upernet
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 40000
Name: upernet_r101_512x512_40k_voc12aug
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
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth

158
configs/vit/vit.yml
View File

@ -1,248 +1,248 @@
Collections:
- Name: vit
Metadata:
- Metadata:
Training Data:
- ADE20K
Name: vit
Models:
- Name: upernet_vit-b16_mln_512x512_80k_ade20k
- Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 144.09
lr schd: 80000
memory (GB): 9.2
Name: upernet_vit-b16_mln_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 131.93
lr schd: 160000
memory (GB): 9.2
Name: upernet_vit-b16_mln_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 146.63
lr schd: 160000
memory (GB): 9.21
Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 33.5
lr schd: 80000
memory (GB): 4.68
Name: upernet_deit-s16_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 34.26
lr schd: 160000
memory (GB): 4.68
Name: upernet_deit-s16_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 89.45
lr schd: 160000
memory (GB): 5.69
Name: upernet_deit-s16_mln_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 80.71
lr schd: 160000
memory (GB): 5.69
Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 103.2
lr schd: 80000
memory (GB): 7.75
Name: upernet_deit-b16_512x512_80k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 96.25
lr schd: 160000
memory (GB): 7.75
Name: upernet_deit-b16_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 128.53
lr schd: 160000
memory (GB): 9.21
Name: upernet_deit-b16_mln_512x512_160k_ade20k
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
Task: Semantic Segmentation
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
- Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
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
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 129.03
lr schd: 160000
memory (GB): 9.21
Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
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
Task: Semantic Segmentation
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