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@@ -141,15 +141,15 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
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* My YOLO:
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-| Model | Scale | Batch | Epoch | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
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-|----------|-------|-------|-------|-------------------------|--------------------|------------------------|-------------------|-------------------|--------------------|--------|
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-| YOLOvx-P | 640 | 4xb32 | 300 | | | | | | | |
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-| YOLOvx-N | 640 | 4xb32 | 300 | | | | | | | |
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-| YOLOvx-T | 640 | 4xb32 | 300 | | | | | | | |
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-| YOLOvx-S | 640 | 4xb32 | 300 | | | | | | | |
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-| YOLOvx-M | 640 | 8xb16 | 300 | | | | | | | |
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-| YOLOvx-L | 640 | 8xb16 | 300 | 50.2 | 68.6 | 50.0 | 68.4 | 176.6 | 47.6 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolovx_l_coco.pth) |
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-| YOLOvx-X | 640 | | | | | | | | | |
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+| Model | Scale | Batch | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
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+|----------|-------|-------|-------------------------|--------------------|------------------------|-------------------|-------------------|--------------------|--------|
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+| YOLOvx-P | 640 | 4xb32 | | | | | | | |
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+| YOLOvx-N | 640 | 4xb32 | | | | | | | |
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+| YOLOvx-T | 640 | 4xb32 | | | | | | | |
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+| YOLOvx-S | 640 | 4xb32 | | | | | | | |
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+| YOLOvx-M | 640 | 8xb16 | | | | | | | |
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+| YOLOvx-L | 640 | 8xb16 | 50.2 | 68.6 | 50.0 | 68.4 | 176.6 | 47.6 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolovx_l_coco.pth) |
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+| YOLOvx-X | 640 | | | | | | | | |
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Due to my limited computing resources, I can not to train `YOLOvx-X` with the setting of `batch size=128`.
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yjh0410