a95f6d8173
* 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
232 lines
7.6 KiB
YAML
232 lines
7.6 KiB
YAML
Collections:
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- Name: UPerNet
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Metadata:
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Training Data:
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- Cityscapes
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- Pascal VOC 2012 + Aug
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- ADE20K
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Models:
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- Name: upernet_r50_512x1024_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 4.25
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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: 77.10
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
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Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
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- Name: upernet_r101_512x1024_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 3.79
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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: 78.69
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
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Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
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- Name: upernet_r50_769x769_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 1.76
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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: 77.98
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
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Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
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- Name: upernet_r101_769x769_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 1.56
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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: 79.03
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
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Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
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- Name: upernet_r50_512x1024_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 4.25
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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: 78.19
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
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Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
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- Name: upernet_r101_512x1024_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 3.79
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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: 79.40
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
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Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
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- Name: upernet_r50_769x769_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 1.76
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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: 79.39
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
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Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
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- Name: upernet_r101_769x769_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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inference time (fps): 1.56
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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: 80.10
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
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Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
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- Name: upernet_r50_512x512_80k_ade20k
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In Collection: UPerNet
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Metadata:
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inference time (fps): 23.40
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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: 40.70
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
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Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
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- Name: upernet_r101_512x512_80k_ade20k
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In Collection: UPerNet
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Metadata:
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inference time (fps): 20.34
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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: 42.91
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
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Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
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- Name: upernet_r50_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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inference time (fps): 23.40
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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: 42.05
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
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Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
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- Name: upernet_r101_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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inference time (fps): 20.34
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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: 43.82
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
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Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
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- Name: upernet_r50_512x512_20k_voc12aug
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In Collection: UPerNet
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Metadata:
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inference time (fps): 23.17
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 74.82
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
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Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
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- Name: upernet_r101_512x512_20k_voc12aug
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In Collection: UPerNet
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Metadata:
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inference time (fps): 19.98
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 77.10
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
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Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
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- Name: upernet_r50_512x512_40k_voc12aug
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In Collection: UPerNet
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Metadata:
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inference time (fps): 23.17
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 75.92
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
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Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
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- Name: upernet_r101_512x512_40k_voc12aug
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In Collection: UPerNet
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Metadata:
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inference time (fps): 19.98
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 77.43
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
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Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
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