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@@ -132,7 +132,7 @@ I have provided a bash file `train_ddp.sh` that enables DDP training. I hope som
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| YOLOv5-M | CSPDarkNet-M | 640 | 250 | | | 74.3 | 25.4 | |
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| YOLOv5-L | CSPDarkNet-L | 640 | 250 | 46.7 | 65.5 | 155.6 | 54.2 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_l_coco.pth) |
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-*I attempted to reproduce the design philosophy of YOLOv5 but may have overlooked some details, leading to poor performance. However, I do not aim to fully replicate YOLOv5's performance, as it is too challenging and resource-intensive for me.*
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+*For **YOLOv5-L**, increasing the batch size should improve performance. Due to my computing resources, I can only set the batch size to 16.*
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* YOLOX:
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@@ -157,6 +157,8 @@ I have provided a bash file `train_ddp.sh` that enables DDP training. I hope som
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- *Due to my limited computing resources, I had to abandon training on other YOLO detectors, including YOLOv7-Huge and YOLOv8-Nano~Large. 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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+- *Using a larger batch size may improve the performance of large models, such as YOLOv5-L, YOLOX-L and YOLOv7-L. Due to my computing resources, I can only set the batch size to 16.*
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+
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## Train
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### Single GPU
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```Shell
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yjh0410