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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.
29 lines
784 B
YAML
29 lines
784 B
YAML
Collections:
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- Metadata:
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Training Data:
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- Cityscapes
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Name: fastscnn
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Models:
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- Config: configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py
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In Collection: fastscnn
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Metadata:
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backbone: Fast-SCNN
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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: 17.71
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lr schd: 160000
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memory (GB): 3.3
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Name: fast_scnn_lr0.12_8x4_160k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 70.96
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mIoU(ms+flip): 72.65
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth
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