updates
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train.py
4
train.py
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@ -417,10 +417,10 @@ def prebias():
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--epochs', type=int, default=273) # 500200 batches at bs 16, 117263 images = 273 epochs
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parser.add_argument('--batch-size', type=int, default=16) # effective bs = batch_size * accumulate = 16 * 4 = 64
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parser.add_argument('--batch-size', type=int, default=1) # effective bs = batch_size * accumulate = 16 * 4 = 64
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parser.add_argument('--accumulate', type=int, default=4, help='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', type=str, default='data/coco.data', help='*.data file path')
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parser.add_argument('--data', type=str, default='data/coco_64img.data', help='*.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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@ -4,10 +4,10 @@
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rm -rf sample_data yolov3 darknet apex coco cocoapi knife knifec
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git clone https://github.com/ultralytics/yolov3
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git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . --user && cd .. && rm -rf apex
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conda install -yc conda-forge scikit-image pycocotools
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sudo conda install -yc conda-forge scikit-image pycocotools
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python3 -c "
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from yolov3.utils.google_utils import gdrive_download
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gdrive_download('1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO','coco.zip')"
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gdrive_download('1WQT6SOktSe8Uw6r10-2JhbEhMY5DJaph','coco.zip')"
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sudo shutdown
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# Re-clone
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