updates
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train.py
4
train.py
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@ -141,7 +141,7 @@ def train(
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# y.append(optimizer.param_groups[0]['lr'])
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# plt.plot(y, label='LambdaLR')
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# plt.xlabel('epoch')
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# plt.xlabel('LR')
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# plt.ylabel('LR')
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# plt.tight_layout()
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# plt.savefig('LR.png', dpi=300)
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@ -315,7 +315,7 @@ if __name__ == '__main__':
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parser.add_argument('--batch-size', type=int, default=8, help='batch size')
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parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate 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_1000img.data', help='coco.data 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('--single-scale', action='store_true', help='train at fixed size (no multi-scale)')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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@ -12,7 +12,7 @@ from tqdm import tqdm
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from . import torch_utils
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matplotlib.rc('font', **{'size': 12})
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matplotlib.rc('font', **{'size': 11})
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# Set printoptions
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torch.set_printoptions(linewidth=1320, precision=5, profile='long')
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