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@@ -115,7 +115,7 @@ python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch
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| Model | Backbone | Scale | Epoch | FPS | 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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-| YOLOv4-Tiny | CSPDarkNet-Tiny | 640 | 250 | | | | | | |
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+| YOLOv4-Tiny | CSPDarkNet-Tiny | 640 | 250 | | | | 8.1 | 2.9 | |
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| YOLOv4 | CSPDarkNet-53 | 640 | 250 | | 46.6 | 65.8 | 162.7 | 61.5 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_coco.pth) |
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* YOLOv5:
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@@ -127,6 +127,8 @@ python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch
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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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+
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* YOLOX:
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| Model | Backbone | Scale | Epoch | FPS | 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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@@ -140,6 +142,10 @@ python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch
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| YOLOv7-T | ELANNet-Tiny | 640 | 300 | | 38.0 | 56.8 | 22.6 | 7.9 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_tiny_coco.pth) |
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| YOLOv7-L | ELANNet-Large | 640 | 300 | | | | 144.6 | 44.0 | |
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+*While YOLOv7 incorporates several technical details, such as anchor box, SimOTA, AuxiliaryHead, and RepConv, I found it too challenging to fully reproduce. Instead, I created a simpler version of YOLOv7 using an anchor-free structure and SimOTA. As a result, my reproduction had poor performance due to the absence of the other technical details. However, since it was only intended as a tutorial, I am not too concerned about this gap.*
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+* Necessary instructions：
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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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- *The reproduced YOLOv5's head is the **Decoupled Head**, which is why the FLOPs and Params are higher than the official YOLOv5. Due to my limited computing resources, I can not align the training configuration with the official YOLOv5, so I cannot fully replicate the official performance. The YOLOv5 I reproduce is for learning purposes only.*
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yjh0410