mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
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
7.5 KiB
YAML
232 lines
7.5 KiB
YAML
Collections:
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- Name: DANet
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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: danet_r50-d8_512x1024_40k_cityscapes
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In Collection: DANet
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Metadata:
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inference time (fps): 2.66
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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: 78.74
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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
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Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
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- Name: danet_r101-d8_512x1024_40k_cityscapes
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In Collection: DANet
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Metadata:
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inference time (fps): 1.99
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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: 80.52
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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
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Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
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- Name: danet_r50-d8_769x769_40k_cityscapes
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In Collection: DANet
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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: 78.88
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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
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Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
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- Name: danet_r101-d8_769x769_40k_cityscapes
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In Collection: DANet
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Metadata:
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inference time (fps): 1.07
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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: 79.88
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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
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Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
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- Name: danet_r50-d8_512x1024_80k_cityscapes
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In Collection: DANet
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Metadata:
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inference time (fps): 2.66
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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: 79.34
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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
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Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
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- Name: danet_r101-d8_512x1024_80k_cityscapes
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In Collection: DANet
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Metadata:
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inference time (fps): 1.99
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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/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
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Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
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- Name: danet_r50-d8_769x769_80k_cityscapes
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In Collection: DANet
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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: 79.27
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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
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Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
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- Name: danet_r101-d8_769x769_80k_cityscapes
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In Collection: DANet
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Metadata:
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inference time (fps): 1.07
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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: 80.47
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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
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Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
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- Name: danet_r50-d8_512x512_80k_ade20k
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In Collection: DANet
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Metadata:
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inference time (fps): 21.20
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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: 41.66
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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
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Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
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- Name: danet_r101-d8_512x512_80k_ade20k
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In Collection: DANet
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Metadata:
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inference time (fps): 14.18
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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: 43.64
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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
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Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
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- Name: danet_r50-d8_512x512_160k_ade20k
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In Collection: DANet
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Metadata:
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inference time (fps): 21.20
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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: 42.45
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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
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Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
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- Name: danet_r101-d8_512x512_160k_ade20k
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In Collection: DANet
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Metadata:
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inference time (fps): 14.18
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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: 44.17
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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
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Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
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- Name: danet_r50-d8_512x512_20k_voc12aug
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In Collection: DANet
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Metadata:
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inference time (fps): 20.94
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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: 74.45
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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
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Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
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- Name: danet_r101-d8_512x512_20k_voc12aug
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In Collection: DANet
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Metadata:
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inference time (fps): 13.76
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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: 76.02
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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
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Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
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- Name: danet_r50-d8_512x512_40k_voc12aug
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In Collection: DANet
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Metadata:
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inference time (fps): 20.94
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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: 76.37
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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
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Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
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- Name: danet_r101-d8_512x512_40k_voc12aug
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In Collection: DANet
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Metadata:
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inference time (fps): 13.76
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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: 76.51
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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
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Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
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