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two places using multi-scale tricks #21
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@Libaishun yes you are correct! Both methods are suitable for performing multi-scale image augmentation. Exact results depend on model, dataset, etc. |
@glenn-jocher setting multi-scale=True in train.py seems use much more cuda memory than disable it. |
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions. |
speed up pycocotools ops
I see you use multi-scale tricks in two places: directly resizing model inputs in train.py and scale image with random_affine in datasets.py, which one is better to use or shoule we use them simultaneously ?
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