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@@ -106,16 +106,14 @@ python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch
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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 | | 37.7 | 56.6 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_tiny_coco.pth) |
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| YOLOv7-Large | ELANNet-Large | 640 | √ | 300 | | | | |
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-| YOLOv8-Nano | CSP-ELANNet-Nano | 640 | √ | 300 | | | | |
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-| YOLOv8-Small | CSP-ELANNet-Small | 640 | √ | 300 | | | | |
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-| YOLOv8-Medium | CSP-ELANNet-Medium | 640 | √ | 300 | | | | |
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-| YOLOv8-Large | CSP-ELANNet-Large | 640 | √ | 300 | | | | |
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+| YOLOv8-Nano | CSP-ELANNet-Nano | 640 | √ | 500 | | | | |
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+| YOLOv8-Large | 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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+*Due to my limited computing resources, I had to abandon training on other YOLO detectors, including YOLOv7-Huge, YOLOv8-Small, YOLOv8-Medium and so on. If you are interested in these models and have trained them using the code from this project, I would greatly appreciate it if you could share the trained weight files with me.*
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## Train
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### Single GPU
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yjh0410