rename this project

README.md

@@ -47,18 +47,6 @@ For example:
 python train.py --cuda -d voc --root path/to/VOCdevkit -m 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 | Weight |
-|--------------|---------------------|-------|------|-------|-------------------|--------------------------|--------|
-| YOLOv1       | ResNet-18           |  640  |  √   |  150  |       76.7        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) |
-| YOLOv2       | DarkNet-19          |  640  |  √   |  150  |       79.8        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) |
-| YOLOv3       | DarkNet-53          |  640  |  √   |  150  |       82.0        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) |
-| YOLOv4       | CSPDarkNet-53       |  640  |  √   |  150  |       83.6        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
-| YOLOX-L      | CSPDarkNet-L        |  640  |  √   |  150  |       84.6        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
-| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       86.0        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_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.*
-
-
 ### COCO
 
 - Download COCO.

README_CN.md

@@ -47,24 +47,11 @@ python dataset/voc.py
 
 - 使用 VOC 训练模型
 
-For example:
+例如:
 ```Shell
 python train.py --cuda -d voc --root path/to/VOC -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
 ```
 
-**P5-Model on COCO:**
-
-| Model        |   Backbone          | Scale |  IP  | Epoch | AP<sup>val<br>0.5 | FPS<sup>3090<br>FP32-bs1 | Weight |
-|--------------|---------------------|-------|------|-------|-------------------|--------------------------|--------|
-| YOLOv1       | ResNet-18           |  640  |  √   |  150  |       76.7        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov1_voc.pth) |
-| YOLOv2       | DarkNet-19          |  640  |  √   |  150  |       79.8        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov2_voc.pth) |
-| YOLOv3       | DarkNet-53          |  640  |  √   |  150  |       82.0        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov3_voc.pth) |
-| YOLOv4       | CSPDarkNet-53       |  640  |  √   |  150  |       83.6        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov4_voc.pth) |
-| YOLOX        | CSPDarkNet-L        |  640  |  √   |  150  |       84.6        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolox_voc.pth) |
-| YOLOv7-Large | ELANNet-Large       |  640  |  √   |  150  |       86.0        |                          | [ckpt](https://github.com/yjh0410/PyTorch_YOLO_Tutorial/releases/download/yolo_tutorial_ckpt/yolov7_large_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。*
-
 ### COCO
 - 下载 COCO.
 ```Shell