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@@ -137,8 +137,8 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
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|---------|---------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
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| YOLOX-N | CSPDarkNet-N | 640 | 300 | 31.1 | 49.5 | 7.5 | 2.3 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_n_coco.pth) |
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| YOLOX-S | CSPDarkNet-S | 640 | 300 | 39.0 | 58.8 | 26.8 | 8.9 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_s_coco.pth) |
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-| YOLOX-M | CSPDarkNet-M | 640 | 300 | | | 74.3 | 25.4 | |
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-| YOLOX-L | CSPDarkNet-L | 640 | 300 | 46.9 | 65.9 | 155.4 | 54.2 | |
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+| YOLOX-M | CSPDarkNet-M | 640 | 300 | 44.6 | 63.8 | 74.3 | 25.4 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_m_coco.pth) |
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+| YOLOX-L | CSPDarkNet-L | 640 | 300 | 46.9 | 65.9 | 155.4 | 54.2 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_l_coco.pth) |
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*For **YOLOX-M** and **YOLOX-L**, increasing the batch size may improve performance. Due to my computing resources, I can only set the batch size to 16.*
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