change document structure

README.md

@@ -2,7 +2,7 @@
   <img src="assets/banner-YOLO.png" align="middle" width = "1000" />
 </p>
 
-
+English | [简体中文](README_cn.md)
 
 ## YOLOv6
 
@@ -47,6 +47,9 @@ YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.
 
 - Speed is tested with TensorRT 8.4 on T4.
 - Precision is figured on models for 300 epochs.
+- Results of the mAP and speed are evaluated on [COCO val2017](https://cocodataset.org/#download) dataset with the input resolution of 640×640.
+- Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6.
+- Params and FLOPs of YOLOv6 are estimated on deployed models.
 
 <details>
 <summary>Legacy models</summary>
@@ -60,12 +63,6 @@ YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.
 
 </details>
 
-- Results of the mAP and speed are evaluated on [COCO val2017](https://cocodataset.org/#download) dataset with the input resolution of 640×640.
-- Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6.
-- Params and FLOPs of YOLOv6 are estimated on deployed models.
-
-
-
 
 ## Quick Start
 
@@ -97,6 +94,23 @@ Multi GPUs (DDP mode recommended)
 ```shell
 python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/dataset.yaml --device 0,1,2,3,4,5,6,7
 ```
+- conf: select config file to specify network/optimizer/hyperparameters. Pretrained model path is recommended to be specified in the config file with the `pretrained` parameter if training on your custom dataset.
+- data: prepare [COCO](http://cocodataset.org) dataset, [YOLO format coco labels](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) and specify dataset paths in data.yaml
+- make sure your dataset structure as follows:
+```
+├── coco
+│   ├── annotations
+│   │   ├── instances_train2017.json
+│   │   └── instances_val2017.json
+│   ├── images
+│   │   ├── train2017
+│   │   └── val2017
+│   ├── labels
+│   │   ├── train2017
+│   │   ├── val2017
+│   ├── LICENSE
+│   ├── README.txt
+```
 
 
 
@@ -167,24 +181,6 @@ Your can also specify a checkpoint path to `--resume` parameter by
 ```
 
 </details>
-
-- conf: select config file to specify network/optimizer/hyperparameters. Pretrained model path is recommended to be specified in the config file with the `pretrained` parameter if training on your custom dataset.
-- data: prepare [COCO](http://cocodataset.org) dataset, [YOLO format coco labels](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) and specify dataset paths in data.yaml
-- make sure your dataset structure as follows:
-```
-├── coco
-│   ├── annotations
-│   │   ├── instances_train2017.json
-│   │   └── instances_val2017.json
-│   ├── images
-│   │   ├── train2017
-│   │   └── val2017
-│   ├── labels
-│   │   ├── train2017
-│   │   ├── val2017
-│   ├── LICENSE
-│   ├── README.txt
-```
 </details>
 
 <details>