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
62 lines
2.0 KiB
YAML
62 lines
2.0 KiB
YAML
Collections:
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- Name: EMANet
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Metadata:
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Training Data:
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- Cityscapes
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Models:
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- Name: emanet_r50-d8_512x1024_80k_cityscapes
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In Collection: EMANet
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Metadata:
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inference time (fps): 4.58
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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: 77.59
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
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Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
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- Name: emanet_r101-d8_512x1024_80k_cityscapes
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In Collection: EMANet
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Metadata:
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inference time (fps): 2.87
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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: 79.10
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
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Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
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- Name: emanet_r50-d8_769x769_80k_cityscapes
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In Collection: EMANet
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Metadata:
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inference time (fps): 1.97
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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/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
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Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
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- Name: emanet_r101-d8_769x769_80k_cityscapes
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In Collection: EMANet
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Metadata:
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inference time (fps): 1.22
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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: 79.62
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
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Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
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