updates
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6
train.py
6
train.py
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@ -76,7 +76,7 @@ def train(
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if multi_scale:
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img_size = round((img_size / 32) * 1.5) * 32 # initiate with maximum multi_scale size
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opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
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# opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
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else:
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torch.backends.cudnn.benchmark = True # unsuitable for multiscale
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@ -308,9 +308,9 @@ if __name__ == '__main__':
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parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
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parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco_32img.data', help='coco.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--img-size', type=int, default=320, help='inference size (pixels)')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
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parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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