208 lines
7.1 KiB
YAML
208 lines
7.1 KiB
YAML
Collections:
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- Name: icnet
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Metadata:
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Training Data:
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- Cityscapes
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Paper:
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URL: https://arxiv.org/abs/1704.08545
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Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
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README: configs/icnet/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
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Version: v0.18.0
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Converted From:
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Code: https://github.com/hszhao/ICNet
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Models:
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- Name: icnet_r18-d8_832x832_80k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-18-D8
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crop size: (832,832)
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lr schd: 80000
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inference time (ms/im):
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- value: 36.87
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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: (832,832)
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memory (GB): 1.7
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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: 68.14
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mIoU(ms+flip): 70.16
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Config: configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
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- Name: icnet_r18-d8_832x832_160k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-18-D8
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crop size: (832,832)
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lr schd: 160000
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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: 71.64
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mIoU(ms+flip): 74.18
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Config: configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
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- Name: icnet_r18-d8_in1k-pre_832x832_80k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-18-D8
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crop size: (832,832)
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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: 72.51
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mIoU(ms+flip): 74.78
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Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
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- Name: icnet_r18-d8_in1k-pre_832x832_160k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-18-D8
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crop size: (832,832)
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lr schd: 160000
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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: 74.43
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mIoU(ms+flip): 76.72
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Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
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- Name: icnet_r50-d8_832x832_80k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-50-D8
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crop size: (832,832)
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lr schd: 80000
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inference time (ms/im):
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- value: 49.8
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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: (832,832)
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memory (GB): 2.53
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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: 68.91
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mIoU(ms+flip): 69.72
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Config: configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
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- Name: icnet_r50-d8_832x832_160k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-50-D8
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crop size: (832,832)
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lr schd: 160000
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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: 73.82
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mIoU(ms+flip): 75.67
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Config: configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
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- Name: icnet_r50-d8_in1k-pre_832x832_80k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-50-D8
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crop size: (832,832)
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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: 74.58
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mIoU(ms+flip): 76.41
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Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
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- Name: icnet_r50-d8_in1k-pre_832x832_160k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-50-D8
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crop size: (832,832)
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lr schd: 160000
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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: 76.29
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mIoU(ms+flip): 78.09
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Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
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- Name: icnet_r101-d8_832x832_80k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-101-D8
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crop size: (832,832)
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lr schd: 80000
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inference time (ms/im):
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- value: 59.0
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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: (832,832)
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memory (GB): 3.08
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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: 70.28
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mIoU(ms+flip): 71.95
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Config: configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
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- Name: icnet_r101-d8_832x832_160k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-101-D8
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crop size: (832,832)
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lr schd: 160000
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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: 73.8
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mIoU(ms+flip): 76.1
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Config: configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
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- Name: icnet_r101-d8_in1k-pre_832x832_80k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-101-D8
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crop size: (832,832)
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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: 75.57
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mIoU(ms+flip): 77.86
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Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
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- Name: icnet_r101-d8_in1k-pre_832x832_160k_cityscapes
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In Collection: icnet
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Metadata:
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backbone: R-101-D8
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crop size: (832,832)
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lr schd: 160000
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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: 76.15
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mIoU(ms+flip): 77.98
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Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth
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