updates
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2
test.py
2
test.py
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@ -209,7 +209,7 @@ if __name__ == '__main__':
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
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parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file')
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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('--batch-size', type=int, default=32, help='size of each image batch')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
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parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
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4
train.py
4
train.py
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@ -213,7 +213,7 @@ def train():
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rect=True,
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cache_labels=True,
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cache_images=opt.cache_images),
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batch_size=batch_size,
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batch_size=batch_size * 2,
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num_workers=nw,
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pin_memory=True,
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collate_fn=dataset.collate_fn)
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@ -326,7 +326,7 @@ def train():
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is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
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results, maps = test.test(cfg,
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data,
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batch_size=batch_size,
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batch_size=batch_size * 2,
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img_size=opt.img_size,
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model=model,
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conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed
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