170 lines
5.4 KiB
YAML
170 lines
5.4 KiB
YAML
Models:
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- Name: fcn_m-v2-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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backbone: M-V2-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: 70.42
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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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Training Memory (GB): 3.4
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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: 61.54
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Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
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- Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
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In Collection: PSPNet
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Metadata:
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backbone: M-V2-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: 89.29
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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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Training Memory (GB): 3.6
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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: 70.23
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Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
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- Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
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In Collection: DeepLabV3
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Metadata:
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backbone: M-V2-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: 119.05
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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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Training Memory (GB): 3.9
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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: 73.84
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Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
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- Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
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In Collection: DeepLabV3+
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Metadata:
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backbone: M-V2-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: 119.05
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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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Training Memory (GB): 5.1
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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: 75.2
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Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
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- Name: fcn_m-v2-d8_512x512_160k_ade20k
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In Collection: FCN
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Metadata:
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backbone: M-V2-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 15.53
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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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Training Memory (GB): 6.5
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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: 19.71
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Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
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- Name: pspnet_m-v2-d8_512x512_160k_ade20k
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In Collection: PSPNet
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Metadata:
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backbone: M-V2-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 17.33
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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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Training Memory (GB): 6.5
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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: 29.68
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Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
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- Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
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In Collection: DeepLabV3
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Metadata:
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backbone: M-V2-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 25.06
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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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Training Memory (GB): 6.8
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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: 34.08
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Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
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- Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
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In Collection: DeepLabV3+
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Metadata:
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backbone: M-V2-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 23.2
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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: (512,512)
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Training Memory (GB): 8.2
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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: 34.02
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Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
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