297 lines
9.4 KiB
YAML
297 lines
9.4 KiB
YAML
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Collections:
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- Name: ccnet
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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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- Pascal VOC 2012 + Aug
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Models:
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- Name: ccnet_r50-d8_512x1024_40k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (512,1024)
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lr schd: 40000
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inference time (ms/im):
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- value: 301.2
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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memory (GB): 6.0
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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: 77.76
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mIoU(ms+flip): 78.87
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Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
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- Name: ccnet_r101-d8_512x1024_40k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (512,1024)
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lr schd: 40000
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inference time (ms/im):
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- value: 432.9
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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memory (GB): 9.5
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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: 76.35
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mIoU(ms+flip): 78.19
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Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
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- Name: ccnet_r50-d8_769x769_40k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (769,769)
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lr schd: 40000
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inference time (ms/im):
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- value: 699.3
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (769,769)
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memory (GB): 6.8
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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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mIoU(ms+flip): 79.93
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Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
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- Name: ccnet_r101-d8_769x769_40k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (769,769)
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lr schd: 40000
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inference time (ms/im):
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- value: 990.1
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (769,769)
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memory (GB): 10.7
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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: 76.94
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mIoU(ms+flip): 78.62
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Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
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- Name: ccnet_r50-d8_512x1024_80k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (512,1024)
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lr schd: 80000
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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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mIoU(ms+flip): 80.16
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Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
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- Name: ccnet_r101-d8_512x1024_80k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (512,1024)
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lr schd: 80000
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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: 78.87
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mIoU(ms+flip): 79.9
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Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
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- Name: ccnet_r50-d8_769x769_80k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (769,769)
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lr schd: 80000
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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: 79.29
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mIoU(ms+flip): 81.08
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Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
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- Name: ccnet_r101-d8_769x769_80k_cityscapes
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (769,769)
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lr schd: 80000
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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: 79.45
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mIoU(ms+flip): 80.66
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Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
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- Name: ccnet_r50-d8_512x512_80k_ade20k
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 47.87
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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memory (GB): 8.8
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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: 41.78
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mIoU(ms+flip): 42.98
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Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
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- Name: ccnet_r101-d8_512x512_80k_ade20k
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 70.87
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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memory (GB): 12.2
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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: 43.97
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mIoU(ms+flip): 45.13
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Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
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- Name: ccnet_r50-d8_512x512_160k_ade20k
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (512,512)
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lr schd: 160000
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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: 42.08
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mIoU(ms+flip): 43.13
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Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
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- Name: ccnet_r101-d8_512x512_160k_ade20k
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (512,512)
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lr schd: 160000
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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: 43.71
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mIoU(ms+flip): 45.04
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Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
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- Name: ccnet_r50-d8_512x512_20k_voc12aug
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (512,512)
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lr schd: 20000
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inference time (ms/im):
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- value: 48.9
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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memory (GB): 6.0
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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: 76.17
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mIoU(ms+flip): 77.51
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Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
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- Name: ccnet_r101-d8_512x512_20k_voc12aug
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (512,512)
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lr schd: 20000
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inference time (ms/im):
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- value: 73.31
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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memory (GB): 9.5
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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: 77.27
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mIoU(ms+flip): 79.02
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Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
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- Name: ccnet_r50-d8_512x512_40k_voc12aug
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In Collection: ccnet
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Metadata:
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backbone: R-50-D8
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crop size: (512,512)
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lr schd: 40000
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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: 75.96
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mIoU(ms+flip): 77.04
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Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
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- Name: ccnet_r101-d8_512x512_40k_voc12aug
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In Collection: ccnet
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Metadata:
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backbone: R-101-D8
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crop size: (512,512)
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lr schd: 40000
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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: 77.87
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mIoU(ms+flip): 78.9
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Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
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