updates
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							|  | @ -40,7 +40,7 @@ def train( | |||
|     latest = weights + 'latest.pt' | ||||
|     best = weights + 'best.pt' | ||||
|     device = torch_utils.select_device() | ||||
|     multi_scale = not opt.single_scale | ||||
|     multi_scale = opt.multi_scale | ||||
| 
 | ||||
|     if multi_scale: | ||||
|         img_size_min = round(img_size / 32 / 1.5) | ||||
|  | @ -306,7 +306,7 @@ if __name__ == '__main__': | |||
|     parser.add_argument('--accumulate', type=int, default=4, help='number of batches to accumulate before optimizing') | ||||
|     parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path') | ||||
|     parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path') | ||||
|     parser.add_argument('--single-scale', action='store_true', help='train at fixed size (no multi-scale)') | ||||
|     parser.add_argument('--multi-scale', action='store_true', help='train at (1/1.5)x - 1.5x sizes') | ||||
|     parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') | ||||
|     parser.add_argument('--rect', action='store_true', help='rectangular training') | ||||
|     parser.add_argument('--resume', action='store_true', help='resume training flag') | ||||
|  |  | |||
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