mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
52b4fa5b9a
* init script * update scripts and generate new yml * fix lint: deeplabv3plus.yml * modify resolution representation * remove field * format crop_size
95 lines
2.8 KiB
YAML
95 lines
2.8 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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backbone: R-50-D8
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 218.34
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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): 5.4
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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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mIoU(ms+flip): 79.44
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Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
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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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- Name: emanet_r101-d8_512x1024_80k_cityscapes
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In Collection: emanet
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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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inference time (ms/im):
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- value: 348.43
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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): 6.2
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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: 79.1
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mIoU(ms+flip): 81.21
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Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
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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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- Name: emanet_r50-d8_769x769_80k_cityscapes
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In Collection: emanet
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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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inference time (ms/im):
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- value: 507.61
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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): 8.9
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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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mIoU(ms+flip): 80.49
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Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
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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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- Name: emanet_r101-d8_769x769_80k_cityscapes
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In Collection: emanet
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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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inference time (ms/im):
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- value: 819.67
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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): 10.1
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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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mIoU(ms+flip): 81.0
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Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
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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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