updates
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train.py
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train.py
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@ -138,7 +138,7 @@ def train(
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# plt.savefig('LR.png', dpi=300)
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# plt.savefig('LR.png', dpi=300)
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# Dataset
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# Dataset
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dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=False, rect=False, image_weights=False)
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dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, rect=False, image_weights=False)
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# Initialize distributed training
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# Initialize distributed training
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if torch.cuda.device_count() > 1:
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if torch.cuda.device_count() > 1:
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@ -61,8 +61,10 @@ sudo shutdown
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# Unit tests
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# Unit tests
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python3 detect.py # detect 2 persons, 1 tie
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python3 detect.py # detect 2 persons, 1 tie
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python3 test.py --data data/coco_32img.data # test mAP = 0.78
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python3 test.py --data data/coco_32img.data # test mAP = 0.8
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python3 train.py --data data/coco_32img.data --epochs 4 --nosave # train 4 epochs
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python3 train.py --data data/coco_32img.data --epochs 5 --nosave # train 5 epochs
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python3 train.py --data data/coco_1cls.data --epochs 5 --nosave # train 5 epochs
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python3 train.py --data data/coco_1img.data --epochs 5 --nosave # train 5 epochs
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# AlexyAB Darknet
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# AlexyAB Darknet
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./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2.cfg darknet53.conv.74 # train
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./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2.cfg darknet53.conv.74 # train
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