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@@ -94,21 +94,21 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
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| YOLOv1 | ResNet-18 | 640 | 150 | 27.9 | 47.5 | 37.8 | 21.3 | [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 | 53.9 | 30.9 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_coco.pth) |
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-* YOLOv3:
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+#### YOLOv3:
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| Model | Backbone | Scale | Epoch | 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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| YOLOv3-Tiny | DarkNet-Tiny | 640 | 250 | 25.4 | 43.4 | 7.0 | 2.3 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_t_coco.pth) |
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| YOLOv3 | DarkNet-53 | 640 | 250 | 42.9 | 63.5 | 167.4 | 54.9 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_coco.pth) |
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-* YOLOv4:
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+#### YOLOv4:
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| Model | Backbone | Scale | Epoch | 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 | 31.0 | 49.1 | 8.1 | 2.9 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_t_coco.pth) |
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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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+#### YOLOv5:
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| Model | Backbone | Scale | Epoch | 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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@@ -119,7 +119,7 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
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*For **YOLOv5-M** and **YOLOv5-L**, increasing the batch size may 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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+#### YOLOX:
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| Model | Backbone | Scale | Epoch | 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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@@ -130,16 +130,16 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
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*For **YOLOX-M** and **YOLOX-L**, increasing the batch size may improve performance. Due to my computing resources, I can only set the batch size to 16.*
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-* YOLOv7:
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+#### YOLOv7:
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| Model | Backbone | Scale | Epoch | 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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| YOLOv7-Tiny | 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 | ELANNet-Large | 640 | 300 | 48.0 | 67.5 | 144.6 | 44.0 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_coco.pth) |
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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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+* 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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-* My YOLO:
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+#### My YOLO:
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| Model | Scale | Batch | AP<sup>test<br>0.5:0.95 | AP<sup>test<br>0.5 | 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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@@ -151,9 +151,11 @@ python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch
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| YOLOvx-L | 640 | 8xb16 | 50.2 | 68.6 | 50.0 | 68.4 | 176.6 | 47.6 | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolovx_l_coco.pth) |
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| YOLOvx-X | 640 | | | | | | | | |
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-Due to my limited computing resources, I can not to train `YOLOvx-X` with the setting of `batch size=128`.
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+- We use `AdamW` optimizer with `per_image_lr=0.001 / 64` and `linear` learning rate decay scheduler to train all models with 300 epoch.
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+- We use `YOLOv5-style Mosaic augmentation` and `YOLOX-style Mixup augmentation` wihout rotation.
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+- Due to my limited computing resources, I can not to train `YOLOvx-X` with the setting of `batch size=128`.
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-* Redesigned RT-DETR:
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+#### Redesigned RT-DETR:
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| Model | Scale | Batch | 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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