charlie880624/STDC
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
STDC/configs/hrnet/hrnet.yml
sennnnn d35fbbdb47
[Enhancement] Add Dev tools to boost develop (#798) 
* Modify default work dir when training.

* Refactor gather_models.py.

* Add train and test matching list.

* Regression benchmark list.

* lower readme name to upper readme name.

* Add url check tool and model inference test tool.

* Modify tool name.

* Support duplicate mode of log json url check.

* Add regression benchmark evaluation automatic tool.

* Add train script generator.

* Only Support script running.

* Add evaluation results gather.

* Add exec Authority.

* Automatically make checkpoint root folder.

* Modify gather results save path.

* Coarse-grained train results gather tool.

* Complete benchmark train script.

* Make some little modifications.

* Fix checkpoint urls.

* Fix unet checkpoint urls.

* Fix fast scnn & fcn checkpoint url.

* Fix fast scnn checkpoint urls.

* Fix fast scnn url.

* Add differential results calculation.

* Add differential results of regression benchmark train results.

* Add an extra argument to select model.

* Update nonlocal_net & hrnet checkpoint url.

* Fix checkpoint url of hrnet and Fix some tta evaluation results and modify gather models tool.

* Modify fast scnn checkpoint url.

* Resolve new comments.

* Fix url check status code bug.

* Resolve some comments.

* Modify train scripts generator.

* Modify work_dir of regression benchmark results.

* model gather tool modification.
2021-09-02 09:44:51 -07:00

441 lines
14 KiB
YAML
Raw Blame History

Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
- Pascal Context
- Pascal Context 59
Name: hrnet
Models:
- Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
inference time (ms/im):
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 73.86
mIoU(ms+flip): 75.91
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
- Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
inference time (ms/im):
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 77.19
mIoU(ms+flip): 78.92
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
- Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
inference time (ms/im):
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 78.48
mIoU(ms+flip): 79.69
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
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 75.31
mIoU(ms+flip): 77.48
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
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 78.65
mIoU(ms+flip): 80.35
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
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 79.93
mIoU(ms+flip): 80.72
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
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 76.31
mIoU(ms+flip): 78.31
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
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 78.8
mIoU(ms+flip): 80.74
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
- 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:
Dataset: Cityscapes
Metrics:
mIoU: 80.65
mIoU(ms+flip): 81.92
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
- Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
inference time (ms/im):
- 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:
Dataset: ADE20K
Metrics:
mIoU: 31.38
mIoU(ms+flip): 32.45
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
- Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
inference time (ms/im):
- 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:
Dataset: ADE20K
Metrics:
mIoU: 36.27
mIoU(ms+flip): 37.28
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
- Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
inference time (ms/im):
- 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:
Dataset: ADE20K
Metrics:
mIoU: 41.9
mIoU(ms+flip): 43.27
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
- 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:
Dataset: ADE20K
Metrics:
mIoU: 33.07
mIoU(ms+flip): 34.56
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
- 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:
Dataset: ADE20K
Metrics:
mIoU: 36.79
mIoU(ms+flip): 38.58
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
- 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:
Dataset: ADE20K
Metrics:
mIoU: 42.02
mIoU(ms+flip): 43.86
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
- Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
inference time (ms/im):
- 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:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 65.5
mIoU(ms+flip): 68.89
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
- Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
inference time (ms/im):
- 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:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.3
mIoU(ms+flip): 74.71
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
- Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
inference time (ms/im):
- 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:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.87
mIoU(ms+flip): 78.58
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
- 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:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 66.61
mIoU(ms+flip): 70.0
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
- 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:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.9
mIoU(ms+flip): 75.59
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
- 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:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.24
mIoU(ms+flip): 78.49
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
- Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
In Collection: hrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (480,480)
inference time (ms/im):
- 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:
Dataset: Pascal Context
Metrics:
mIoU: 45.14
mIoU(ms+flip): 47.42
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
- 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:
Dataset: Pascal Context
Metrics:
mIoU: 45.84
mIoU(ms+flip): 47.84
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
- 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:
Dataset: Pascal Context 59
Metrics:
mIoU: 50.33
mIoU(ms+flip): 52.83
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
- 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:
Dataset: Pascal Context 59
Metrics:
mIoU: 51.12
mIoU(ms+flip): 53.56
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
Reference in New Issue View Git Blame Copy Permalink