updates
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@ -167,7 +167,7 @@ if __name__ == '__main__':
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parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
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parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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opt = parser.parse_args()
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opt = parser.parse_args()
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@ -830,7 +830,7 @@ def print_mutation(hyp, results, bucket=''):
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def apply_classifier(x, model, img, im0):
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def apply_classifier(x, model, img, im0):
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# applies a second stage classifier to yolo outputs
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# applies a second stage classifier to yolo outputs
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im0 = [im0] if isinstance(im0, np.ndarray) else im0
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for i, d in enumerate(x): # per image
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for i, d in enumerate(x): # per image
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if d is not None and len(d):
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if d is not None and len(d):
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d = d.clone()
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d = d.clone()
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