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@@ -98,18 +98,23 @@ 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 | Backbone | Scale | IP | Epoch | FPS | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | Weight |
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-|--------------|---------------|-------|------|-------|-------|------------------------|-------------------|--------|
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-| YOLOv1 | ResNet-18 | 640 | √ | 150 | | 27.9 | 47.5 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_coco.pth) |
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-| YOLOv2 | DarkNet-19 | 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 | DarkNet-53 | 640 | √ | 250 | | 42.9 | 63.5 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_coco.pth) |
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-| YOLOv4 | CSPDarkNet-L | 640 | √ | 250 | | 46.6 | 65.8 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_coco.pth) |
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-| YOLOv5 | CSPDarkNet-53 | 640 | √ | 250 | | | | |
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-| YOLOX | CSPDarkNet-L | 640 | √ | 300 | | 46.6 | 66.1 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_coco.pth) |
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-| YOLOv7-Nano | ELANNet-Nano | 640 | √ | 300 | | | | |
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-| YOLOv7-Tiny | ELANNet-Tiny | 640 | √ | 300 | | | | |
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-| YOLOv7-Large | ELANNet-Large | 640 | √ | 300 | | | | |
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-| YOLOv7-Huge | ELANNet-Huge | 640 | √ | 300 | | | | |
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+| Model | Backbone | Scale | IP | Epoch | FPS | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | Weight |
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+|---------------|--------------------|-------|------|-------|-------|------------------------|-------------------|--------|
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+| YOLOv1 | ResNet-18 | 640 | √ | 150 | | 27.9 | 47.5 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_coco.pth) |
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+| YOLOv2 | DarkNet-19 | 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 | DarkNet-53 | 640 | √ | 250 | | 42.9 | 63.5 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_coco.pth) |
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+| YOLOv4 | CSPDarkNet-L | 640 | √ | 250 | | 46.6 | 65.8 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_coco.pth) |
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+| YOLOv5 | CSPDarkNet-53 | 640 | √ | 250 | | | | |
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+| YOLOX | CSPDarkNet-L | 640 | √ | 300 | | 46.6 | 66.1 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_coco.pth) |
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+| YOLOv7-Nano | ELANNet-Nano | 640 | √ | 300 | | | | |
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+| YOLOv7-Tiny | ELANNet-Tiny | 640 | √ | 300 | | | | |
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+| YOLOv7-Large | ELANNet-Large | 640 | √ | 300 | | | | |
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+| YOLOv7-Huge | ELANNet-Huge | 640 | √ | 300 | | | | |
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+| YOLOv8-Nano | CSP-ELANNet-Nano | 640 | × | 500 | | | | |
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+| YOLOv8-Small | CSP-ELANNet-Small | 640 | × | 500 | | | | |
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+| YOLOv8-Medium | CSP-ELANNet-Medium | 640 | × | 500 | | | | |
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+| YOLOv8-Large | CSP-ELANNet-Large | 640 | × | 500 | | | | |
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+| YOLOv8-Huge | CSP-ELANNet-Large | 640 | × | 500 | | | | |
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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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