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
520 lines
17 KiB
YAML
520 lines
17 KiB
YAML
Collections:
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- Name: FCN
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Metadata:
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Training Data:
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- Cityscapes
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- Pascal Context
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- Pascal VOC 2012 + Aug
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- ADE20K
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- Name: FCN-D6
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Metadata:
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Training Data:
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- Cityscapes
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- Pascal Context
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- Pascal VOC 2012 + Aug
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- ADE20K
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Models:
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- Name: fcn_r50-d8_512x1024_40k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 4.17
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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: 72.25
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
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Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py
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- Name: fcn_r101-d8_512x1024_40k_cityscapes
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In Collection: FCN
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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: 75.45
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
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Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py
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- Name: fcn_r50-d8_769x769_40k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 1.80
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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: 71.47
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
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Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py
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- Name: fcn_r101-d8_769x769_40k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 1.19
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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: 73.93
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
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Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py
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- Name: fcn_r18-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 14.65
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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: 71.11
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
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Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py
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- Name: fcn_r50-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 4.17
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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: 73.61
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
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Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py
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- Name: fcn_r101-d8_512x1024_80k_cityscapes
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In Collection: FCN
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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: 75.13
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
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Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py
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- Name: fcn_r18-d8_769x769_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 6.40
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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: 70.80
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
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Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py
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- Name: fcn_r50-d8_769x769_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 1.80
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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: 72.64
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
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Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py
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- Name: fcn_r101-d8_769x769_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 1.19
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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: 75.52
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
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Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py
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- Name: fcn_r18b-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 16.74
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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: 70.24
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
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Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py
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- Name: fcn_r50b-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 4.20
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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: 75.65
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
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Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py
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- Name: fcn_r101b-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 2.73
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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: 77.37
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
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Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
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- Name: fcn_r18b-d8_769x769_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 6.70
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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: 69.66
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
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Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py
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- Name: fcn_r50b-d8_769x769_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 1.82
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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: 73.83
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
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Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py
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- Name: fcn_r101b-d8_769x769_80k_cityscapes
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In Collection: FCN
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Metadata:
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inference time (fps): 1.15
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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: 77.02
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
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Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py
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- Name: fcn_d6_r50-d16_512x1024_40k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 10.22
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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: 77.06
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
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Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes.py
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- Name: fcn_d6_r50-d16_512x1024_80k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 10.35
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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: 77.27
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth
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Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes.py
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- Name: fcn_d6_r50-d16_769x769_40k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 4.17
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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: 76.82
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth
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Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes.py
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- Name: fcn_d6_r50-d16_769x769_80k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 4.15
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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: 77.04
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth
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Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes.py
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- Name: fcn_d6_r101-d16_512x1024_40k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 8.04
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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: 77.36
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth
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Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
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- Name: fcn_d6_r101-d16_512x1024_80k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 8.26
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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: 78.46
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth
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Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes.py
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- Name: fcn_d6_r101-d16_769x769_40k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 3.12
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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: 77.28
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth
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Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes.py
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- Name: fcn_d6_r101-d16_769x769_80k_cityscapes
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In Collection: FCN-D6
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Metadata:
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inference time (fps): 3.21
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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: 78.06
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth
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Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes.py
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- Name: fcn_r50-d8_512x512_80k_ade20k
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In Collection: FCN
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Metadata:
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inference time (fps): 23.49
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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: 35.94
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
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Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py
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- Name: fcn_r101-d8_512x512_80k_ade20k
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In Collection: FCN
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Metadata:
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inference time (fps): 14.78
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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: 39.61
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
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Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py
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- Name: fcn_r50-d8_512x512_160k_ade20k
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In Collection: FCN
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Metadata:
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inference time (fps): 23.49
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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: 36.10
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
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Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py
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- Name: fcn_r101-d8_512x512_160k_ade20k
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In Collection: FCN
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Metadata:
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inference time (fps): 14.78
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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: 39.91
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
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Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
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- Name: fcn_r50-d8_512x512_20k_voc12aug
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In Collection: FCN
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Metadata:
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inference time (fps): 23.28
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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: 67.08
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
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Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py
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- Name: fcn_r101-d8_512x512_20k_voc12aug
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In Collection: FCN
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Metadata:
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inference time (fps): 14.81
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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: 71.16
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
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Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py
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- Name: fcn_r50-d8_512x512_40k_voc12aug
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In Collection: FCN
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Metadata:
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inference time (fps): 23.28
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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: 66.97
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
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Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py
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- Name: fcn_r101-d8_512x512_40k_voc12aug
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In Collection: FCN
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Metadata:
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inference time (fps): 14.81
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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: 69.91
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
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Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py
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- Name: fcn_r101-d8_480x480_40k_pascal_context
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In Collection: FCN
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Metadata:
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inference time (fps): 9.93
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal Context
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Metrics:
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mIoU: 44.43
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth
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Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py
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- Name: fcn_r101-d8_480x480_80k_pascal_context
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In Collection: FCN
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Metadata:
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inference time (fps): 9.93
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal Context
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Metrics:
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mIoU: 44.13
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth
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Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py
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- Name: fcn_r101-d8_480x480_40k_pascal_context_59
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In Collection: FCN
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Metadata:
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inference time (fps): None
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal Context
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Metrics:
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mIoU: 48.42
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
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Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py
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- Name: fcn_r101-d8_480x480_80k_pascal_context_59
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In Collection: FCN
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Metadata:
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inference time (fps): None
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal Context
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Metrics:
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mIoU: 49.35
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
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Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py
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