mirror of
https://github.com/open-mmlab/mmsegmentation.git
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* 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
175 lines
5.5 KiB
YAML
175 lines
5.5 KiB
YAML
Collections:
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- Name: dnl
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Metadata:
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Training Data:
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- Cityscapes
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- ADE20K
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Models:
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- Name: dnl_r50-d8_512x1024_40k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 2.56
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.61
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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
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Config: configs/dnl/dnl_r50-d8_512x1024_40k_cityscapes.py
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- Name: dnl_r101-d8_512x1024_40k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 1.96
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.31
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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
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Config: configs/dnl/dnl_r101-d8_512x1024_40k_cityscapes.py
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- Name: dnl_r50-d8_769x769_40k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 1.50
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.44
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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
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Config: configs/dnl/dnl_r50-d8_769x769_40k_cityscapes.py
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- Name: dnl_r101-d8_769x769_40k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 1.02
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 76.39
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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
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Config: configs/dnl/dnl_r101-d8_769x769_40k_cityscapes.py
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- Name: dnl_r50-d8_512x1024_80k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 2.56
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.33
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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
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Config: configs/dnl/dnl_r50-d8_512x1024_80k_cityscapes.py
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- Name: dnl_r101-d8_512x1024_80k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 1.96
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 80.41
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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
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Config: configs/dnl/dnl_r101-d8_512x1024_80k_cityscapes.py
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- Name: dnl_r50-d8_769x769_80k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 1.50
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.36
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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
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Config: configs/dnl/dnl_r50-d8_769x769_80k_cityscapes.py
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- Name: dnl_r101-d8_769x769_80k_cityscapes
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In Collection: dnl
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Metadata:
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inference time (fps): 1.02
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.41
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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
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Config: configs/dnl/dnl_r101-d8_769x769_80k_cityscapes.py
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- Name: dnl_r50-d8_512x512_80k_ade20k
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In Collection: dnl
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Metadata:
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inference time (fps): 20.66
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 41.76
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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
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Config: configs/dnl/dnl_r50-d8_512x512_80k_ade20k.py
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- Name: dnl_r101-d8_512x512_80k_ade20k
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In Collection: dnl
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Metadata:
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inference time (fps): 12.54
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 43.76
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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
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Config: configs/dnl/dnl_r101-d8_512x512_80k_ade20k.py
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- Name: dnl_r50-d8_512x512_160k_ade20k
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In Collection: dnl
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Metadata:
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inference time (fps): 20.66
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 41.87
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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
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Config: configs/dnl/dnl_r50-d8_512x512_160k_ade20k.py
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- Name: dnl_r101-d8_512x512_160k_ade20k
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In Collection: dnl
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Metadata:
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inference time (fps): 12.54
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 44.25
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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
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Config: configs/dnl/dnl_r101-d8_512x512_160k_ade20k.py
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