谢昕辰 a95f6d8173
[Feature] support mim (#549)
* dice loss

* format code, add docstring and calculate denominator without valid_mask

* minor change

* restore

* add metafile

* add manifest.in and add config at setup.py

* add requirements

* modify manifest

* modify manifest

* Update MANIFEST.in

* add metafile

* add metadata

* fix typo

* Update metafile.yml

* Update metafile.yml

* minor change

* Update metafile.yml

* add subfix

* fix mmshow

* add more  metafile

* add config to model_zoo

* fix bug

* Update mminstall.txt

* [fix] Add models

* [Fix] Add collections

* [fix] Modify collection name

* [Fix] Set datasets to unet metafile

* [Fix] Modify collection names

* complement inference time
2021-05-31 15:07:24 -07:00

175 lines
5.5 KiB
YAML

Collections:
- Name: dnl
Metadata:
Training Data:
- Cityscapes
- ADE20K
Models:
- Name: dnl_r50-d8_512x1024_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 2.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.61
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py
- Name: dnl_r101-d8_512x1024_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.31
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py
- Name: dnl_r50-d8_769x769_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.44
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py
- Name: dnl_r101-d8_769x769_40k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.02
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.39
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py
- Name: dnl_r50-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 2.56
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.33
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py
- Name: dnl_r101-d8_512x1024_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.96
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py
- Name: dnl_r50-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.50
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.36
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py
- Name: dnl_r101-d8_769x769_80k_cityscapes
In Collection: dnl
Metadata:
inference time (fps): 1.02
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.41
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py
- Name: dnl_r50-d8_512x512_80k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 20.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py
- Name: dnl_r101-d8_512x512_80k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 12.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py
- Name: dnl_r50-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 20.66
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py
- Name: dnl_r101-d8_512x512_160k_ade20k
In Collection: dnl
Metadata:
inference time (fps): 12.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.25
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py