2aa632ebe7
* [Enhancement] Change readme style and prepare for metafiles update. * Update apcnet github repo url. * add code snippet. * split code snippet & official repo. * update md2yml hook. * Update metafiles. * Add converted from attribute. * process conflict. * Put defualt variable value. * update bisenet v2 metafile. * checkout to ubuntu environment. * pop empty attribute & make task attribute list. * update readme style * update readme style * update metafiles Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
104 lines
3.3 KiB
YAML
104 lines
3.3 KiB
YAML
Collections:
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- Name: mobilenet_v3
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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/1801.04381
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Title: Searching for MobileNetV3
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README: configs/mobilenet_v3/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
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Version: v0.17.0
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Converted From:
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Code: https://github.com/tensorflow/models/tree/master/research/deeplab
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Models:
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- Name: lraspp_m-v3-d8_512x1024_320k_cityscapes
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In Collection: mobilenet_v3
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Metadata:
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backbone: M-V3-D8
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crop size: (512,1024)
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lr schd: 320000
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inference time (ms/im):
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- value: 65.7
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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): 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: 69.54
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mIoU(ms+flip): 70.89
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Config: configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
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- Name: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes
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In Collection: mobilenet_v3
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Metadata:
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backbone: M-V3-D8 (scratch)
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crop size: (512,1024)
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lr schd: 320000
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inference time (ms/im):
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- value: 67.7
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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): 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: 67.87
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mIoU(ms+flip): 69.78
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Config: configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
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- Name: lraspp_m-v3s-d8_512x1024_320k_cityscapes
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In Collection: mobilenet_v3
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Metadata:
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backbone: M-V3s-D8
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crop size: (512,1024)
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lr schd: 320000
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inference time (ms/im):
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- value: 42.3
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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): 5.3
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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: 64.11
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mIoU(ms+flip): 66.42
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Config: configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
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- Name: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes
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In Collection: mobilenet_v3
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Metadata:
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backbone: M-V3s-D8 (scratch)
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crop size: (512,1024)
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lr schd: 320000
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inference time (ms/im):
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- value: 40.82
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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): 5.3
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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: 62.74
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mIoU(ms+flip): 65.01
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Config: configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
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