d35fbbdb47
* Modify default work dir when training. * Refactor gather_models.py. * Add train and test matching list. * Regression benchmark list. * lower readme name to upper readme name. * Add url check tool and model inference test tool. * Modify tool name. * Support duplicate mode of log json url check. * Add regression benchmark evaluation automatic tool. * Add train script generator. * Only Support script running. * Add evaluation results gather. * Add exec Authority. * Automatically make checkpoint root folder. * Modify gather results save path. * Coarse-grained train results gather tool. * Complete benchmark train script. * Make some little modifications. * Fix checkpoint urls. * Fix unet checkpoint urls. * Fix fast scnn & fcn checkpoint url. * Fix fast scnn checkpoint urls. * Fix fast scnn url. * Add differential results calculation. * Add differential results of regression benchmark train results. * Add an extra argument to select model. * Update nonlocal_net & hrnet checkpoint url. * Fix checkpoint url of hrnet and Fix some tta evaluation results and modify gather models tool. * Modify fast scnn checkpoint url. * Resolve new comments. * Fix url check status code bug. * Resolve some comments. * Modify train scripts generator. * Modify work_dir of regression benchmark results. * model gather tool modification.
91 lines
2.8 KiB
YAML
91 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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Name: fp16
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Models:
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- Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
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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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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: 115.74
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lr schd: 80000
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memory (GB): 5.37
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Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 76.8
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Task: Semantic Segmentation
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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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- Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
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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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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: 114.03
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lr schd: 80000
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memory (GB): 5.34
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Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.46
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Task: Semantic Segmentation
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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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- Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
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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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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: 259.07
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lr schd: 80000
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memory (GB): 5.75
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Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 80.48
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Task: Semantic Segmentation
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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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- Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
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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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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: 127.06
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lr schd: 80000
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memory (GB): 6.35
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Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 80.46
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Task: Semantic Segmentation
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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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