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* [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>
100 lines
3.1 KiB
YAML
100 lines
3.1 KiB
YAML
Collections:
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- Name: fp16
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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/1710.03740
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Title: Mixed Precision Training
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README: configs/fp16/README.md
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Code:
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URL: https://github.com/open-mmlab/mmcv/blob/v1.3.14/mmcv/runner/hooks/optimizer.py#L134
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Version: v1.3.14
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Converted From:
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Code: https://github.com/baidu-research/DeepBench
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Models:
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- Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
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In Collection: fp16
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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: 115.74
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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): 5.37
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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: 76.8
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Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230921-50245227.pth
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- Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
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In Collection: fp16
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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: 114.03
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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): 5.34
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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: 79.46
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Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230919-ade37931.pth
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- Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
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In Collection: fp16
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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: 259.07
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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): 5.75
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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: 80.48
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Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920-bc86dc84.pth
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- Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
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In Collection: fp16
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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: 127.06
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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: (512,1024)
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memory (GB): 6.35
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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: 80.46
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Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920-cc58bc8d.pth
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