update README

README.md

@@ -64,15 +64,15 @@ For example:
 python train.py --cuda -d voc --root path/to/VOCdevkit -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
 ```
 
-| Model  |   Backbone    | Scale |  IP  | Epoch | AP<sup>val<br>0.5 | FPS<sup>3090<br>FP32-bs1 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
-|--------|---------------|-------|------|-------|-------------------|--------------------------|-------------------|--------------------|--------|
-| YOLOv1 | ResNet-18     |  640  |  √   |  150  |       76.7        |                          |   37.8            |   21.3             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) |
-| YOLOv2 | DarkNet-19    |  640  |  √   |  150  |       79.8        |                          |   53.9            |   30.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) |
-| YOLOv3 | DarkNet-53    |  640  |  √   |  150  |       82.0        |                          |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) |
-| YOLOv4 | CSPDarkNet-53 |  640  |  √   |  150  |       83.6        |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
-| YOLOv5 | CSPDarkNet-L  |  640  |  √   |  150  |       83.8        |                          |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_voc.pth) |
-| YOLOX  | CSPDarkNet-L  |  640  |  √   |  150  |       84.6        |                          |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
-| YOLOv7 | ELANNet       |  640  |  √   |  150  |       85.5        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_voc.pth) |
+| Model        |   Backbone          | Scale |  IP  | Epoch | AP<sup>val<br>0.5 | FPS<sup>3090<br>FP32-bs1 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
+|--------------|---------------------|-------|------|-------|-------------------|--------------------------|-------------------|--------------------|--------|
+| YOLOv1       | ResNet-18           |  640  |  √   |  150  |       76.7        |                          |   37.8            |   21.3             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) |
+| YOLOv2       | DarkNet-19          |  640  |  √   |  150  |       79.8        |                          |   53.9            |   30.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) |
+| YOLOv3       | DarkNet-53          |  640  |  √   |  150  |       82.0        |                          |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) |
+| YOLOv4       | CSPDarkNet-53       |  640  |  √   |  150  |       83.6        |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
+| YOLOv5       | CSPDarkNet-L        |  640  |  √   |  150  |       83.8        |                          |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_voc.pth) |
+| YOLOX        | CSPDarkNet-L        |  640  |  √   |  150  |       84.6        |                          |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
+| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       85.5        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_voc.pth) |
 
 *All models are trained with ImageNet pretrained weight (IP). All FLOPs are measured with a 640x640 image size on VOC2007 test. The FPS is measured with batch size 1 on 3090 GPU from the model inference to the NMS operation.*
 

README_CN.md

@@ -64,15 +64,15 @@ python train.py --cuda -d voc --root path/to/VOC -v yolov1 -bs 16 --max_epoch 15
 
 **P5-Model on COCO:**
 
-| Model  |   Backbone    | Scale |  IP  | Epoch | AP<sup>val<br>0.5 | FPS<sup>3090<br>FP32-bs1 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
-|--------|---------------|-------|------|-------|-------------------|--------------------------|-------------------|--------------------|--------|
-| YOLOv1 | ResNet-18     |  640  |  √   |  150  |       76.7        |                          |   37.8            |   21.3             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) |
-| YOLOv2 | DarkNet-19    |  640  |  √   |  150  |       79.8        |                          |   53.9            |   30.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) |
-| YOLOv3 | DarkNet-53    |  640  |  √   |  150  |       82.0        |                          |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) |
-| YOLOv4 | CSPDarkNet-53 |  640  |  √   |  150  |       83.6        |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
-| YOLOv5 | CSPDarkNet-L  |  640  |  √   |  150  |       83.8        |                          |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_voc.pth) |
-| YOLOX  | CSPDarkNet-L  |  640  |  √   |  150  |       84.6        |                          |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
-| YOLOv7 | ELANNet       |  640  |  √   |  150  |       85.5        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_voc.pth) |
+| Model        |   Backbone          | Scale |  IP  | Epoch | AP<sup>val<br>0.5 | FPS<sup>3090<br>FP32-bs1 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
+|--------------|---------------------|-------|------|-------|-------------------|--------------------------|-------------------|--------------------|--------|
+| YOLOv1       | ResNet-18           |  640  |  √   |  150  |       76.7        |                          |   37.8            |   21.3             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) |
+| YOLOv2       | DarkNet-19          |  640  |  √   |  150  |       79.8        |                          |   53.9            |   30.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) |
+| YOLOv3       | DarkNet-53          |  640  |  √   |  150  |       82.0        |                          |   167.4           |   54.9             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) |
+| YOLOv4       | CSPDarkNet-53       |  640  |  √   |  150  |       83.6        |                          |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
+| YOLOv5       | CSPDarkNet-L        |  640  |  √   |  150  |       83.8        |                          |   155.6           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov5_voc.pth) |
+| YOLOX        | CSPDarkNet-L        |  640  |  √   |  150  |       84.6        |                          |   155.4           |   54.2             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
+| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       85.5        |                          |   144.6           |   44.0             | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_voc.pth) |
 
 *所有的模型都使用了ImageNet预训练权重（IP），所有的FLOPs都是在VOC2007 test数据集上以640x640或1280x1280的输入尺寸来测试的。FPS指标是在一张3090型号的GPU上以batch size=1的输入来测试的，请注意，测速的内容包括模型前向推理、后处理以及NMS操作。*