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@@ -96,13 +96,13 @@ For example:
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python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
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```
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-| Model | Scale | IP | Epoch | AP<sup>val<br>0.5:0.95 | AP<sup>test<br>0.5:0.95 | Weight |
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-|--------|-------|------|-------|------------------------|-------------------------|--------|
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+| Model | Scale | IP | Epoch | AP<sup>val<br>0.5:0.95 | AP<sup>test<br>50 | Weight |
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+|--------|-------|------|-------|------------------------|-------------------|--------|
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| YOLOv1 | 640 | √ | 150 | | | |
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-| YOLOv2 | 640 | √ | 150 | | | |
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-| YOLOv3 | 640 | √ | 150 | | | |
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-| YOLOv4 | 640 | √ | 150 | | | |
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-| YOLOX | 640 | √ | 150 | | | |
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+| YOLOv2 | 640 | √ | 150 | 32.7 | 50.9 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_coco.pth) |
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+| YOLOv3 | 640 | √ | 250 | | | |
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+| YOLOv4 | 640 | √ | 250 | | | |
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+| YOLOX | 640 | √ | 250 | | | |
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*All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on COCO val2017. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.*
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