84 lines
2.3 KiB
YAML
84 lines
2.3 KiB
YAML
Collections:
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- Name: FPN
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Metadata:
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Training Data:
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- Cityscapes
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- Pascal VOC 2012 + Aug
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- ADE20K
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Models:
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- Name: fpn_r50_512x1024_80k_cityscapes
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In Collection: FPN
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Metadata:
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inference time (ms/im):
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- value: 73.86
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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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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 74.52
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
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Config: configs/fpn/fpn_r50_512x1024_80k_cityscapes.py
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- Name: fpn_r101_512x1024_80k_cityscapes
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In Collection: FPN
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Metadata:
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inference time (ms/im):
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- value: 97.18
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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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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 75.80
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
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Config: configs/fpn/fpn_r101_512x1024_80k_cityscapes.py
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- Name: fpn_r50_512x512_160k_ade20k
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In Collection: FPN
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Metadata:
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inference time (ms/im):
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- value: 17.93
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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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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 37.49
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
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Config: configs/fpn/fpn_r50_512x512_160k_ade20k.py
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- Name: fpn_r101_512x512_160k_ade20k
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In Collection: FPN
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Metadata:
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inference time (ms/im):
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- value: 24.64
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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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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 39.35
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
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Config: configs/fpn/fpn_r101_512x512_160k_ade20k.py
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