updates
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2
test.py
2
test.py
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@ -43,7 +43,7 @@ def test(
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# Dataloader
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# Dataloader
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dataset = LoadImagesAndLabels(test_path, img_size=img_size)
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dataset = LoadImagesAndLabels(test_path, img_size=img_size)
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dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=0)
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dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=4)
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mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
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mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
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train.py
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train.py
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@ -205,7 +205,7 @@ if __name__ == '__main__':
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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('--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=32 * 13, help='pixels')
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parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--num-workers', type=int, default=0, help='number of Pytorch DataLoader workers')
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parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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opt = parser.parse_args()
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opt = parser.parse_args()
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print(opt, end='\n\n')
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print(opt, end='\n\n')
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