96 lines
2.8 KiB
YAML
96 lines
2.8 KiB
YAML
Collections:
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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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Name: point_rend
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Models:
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- Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
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In Collection: point_rend
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Metadata:
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backbone: R-50
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crop size: (512,1024)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,1024)
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value: 117.92
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lr schd: 80000
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memory (GB): 3.1
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Name: pointrend_r50_512x1024_80k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 76.47
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mIoU(ms+flip): 78.13
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
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- Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
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In Collection: point_rend
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Metadata:
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backbone: R-101
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crop size: (512,1024)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,1024)
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value: 142.86
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lr schd: 80000
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memory (GB): 4.2
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Name: pointrend_r101_512x1024_80k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.3
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mIoU(ms+flip): 79.97
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
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- Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
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In Collection: point_rend
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Metadata:
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backbone: R-50
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 57.77
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lr schd: 160000
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memory (GB): 5.1
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Name: pointrend_r50_512x512_160k_ade20k
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Results:
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Dataset: ADE20K
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Metrics:
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mIoU: 37.64
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mIoU(ms+flip): 39.17
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
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- Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
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In Collection: point_rend
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Metadata:
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backbone: R-101
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crop size: (512,512)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (512,512)
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value: 64.52
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lr schd: 160000
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memory (GB): 6.1
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Name: pointrend_r101_512x512_160k_ade20k
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Results:
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Dataset: ADE20K
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Metrics:
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mIoU: 40.02
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mIoU(ms+flip): 41.6
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
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