Update README.md

Author: JackWoo0831

README.md

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-  
+# YOLO v7 + 各种tracker实现多目标跟踪
+
+## 亮点  
+1. 统一代码风格, 对多种tracker重新整理, 详细注释, 方便阅读, 适合初学者 
+2. 多类多目标跟踪 
+3. 各种tracker集成在一个文件夹"./tracker/"内, 方便移植到其他detector.  
+
+## 集成的tracker:
+SORT,  
+DeepSORT,  
+ByteTrack([ECCV2022](https://arxiv.org/pdf/2110.06864)),  
+DeepMOT([CVPR2020](https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi-Object_Tracker_CVPR_2020_paper.pdf)),  
+BoT-SORT([arxiv2206](https://arxiv.org/pdf/2206.14651.pdf)), 
+
+
+## TODO
+- [ ] 集成UAVMOT([CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Multi-Object_Tracking_Meets_Moving_UAV_CVPR_2022_paper.pdf))
+- [ ] 达到更好的结果(缓解类别不平衡, 小目标等等)...
+- [ ] MOT challenge数据集
+
+
+## 效果
+在VisDrone2019-MOT train训练约10 epochs, 采用YOLO v7 w6结构, COCO预训练模型基础上训练. GPU: single Tesla A100, 每个epoch约40min.  
+在VisDrone2019-MOT test dev测试, 跟踪所有的类别. 
+
+YOLO v7 VisDrone训练完模型: 
+> 链接：https://pan.baidu.com/s/1m13Q8Lx_hrPVFZI6lLDrWQ 
+> 提取码：ndkf
+
+| Tracker       | MOTA   | IDF1 | IDS | fps |
+|:--------------|:-------:|:------:|:------:|:------:|
+|SORT       | **26.4**   | 36.4 |3264 |12.2 |
+|DeepSORT  | 12.1   | 26.9 | 3860 | 12.4|
+|ByteTrack  | 25.1   | 40.8| 1590 | **14.32**|
+|DeepMOT  | 15.0  | 24.8|3666 |7.64|
+|BoT-SORT  | 23.0 | **41.4**|**1014** |5.41|
+
+> fps具有一定的随机性
+
+![gif](https://github.com/JackWoo0831/Yolov7-tracker/blob/master/test2.gif)
+
+
+## 训练
+
+训练遵循YOLO v7的训练方式, 数据集格式可以参照[YOLO v5 train custom data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  
+即数据集文件遵循
+```shell
+class x_center y_center width height
+```  
+其中x_center y_center width height必须是**归一化**的.  
+如果您训练VisDrone数据集, 可以直接调用:  
+```shell
+python tools/convert_VisDrone_to_yolov2.py --split_name VisDrone2019-MOT-train --generate_imgs
+```  
+> 需要您修改一些路径变量.  
+
+准备好数据集后, 假如训练YOLO v7-w6模型(single GPU):  
+```shell
+python train_aux.py --dataset visdrone --workers 8 --device <$GPU_id$> --batch-size 16 --data data/visdrone_all.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights <$YOLO v7 pretrained model path$> --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
+```  
+> 更多训练信息参考[YOLO v7](https://github.com/WongKinYiu/yolov7)
+
+## 跟踪  
+
+> model_path 参数为训练后的detector model, 假设路径为 runs/train/yolov7-w6-custom4/weights/best.pt  
+
+***SORT*** : 
+```shell
+python tracker/track.py --dataset visdrone --data_format origin --tracker sort --model_path runs/train/yolov7-w6-custom4/weights/best.pt
+```  
+
+***DeepSORT***:  
+```shell
+python tracker/track.py --dataset visdrone --data_format origin --tracker deepsort --model_path runs/train/yolov7-w6-custom4/weights/best.pt
+```
+
+***ByteTrack***:  
+```shell
+python tracker/track.py --dataset visdrone --data_format origin --tracker bytetrack --model_path runs/train/yolov7-w6-custom4/weights/best.pt 
+```
+
+***DeepMOT***:  
+```shell
+python tracker/track.py --dataset visdrone --data_format origin --tracker deepmot --model_path runs/train/yolov7-w6-custom4/weights/best.pt
+```
+
+***BoT-SORT***:  
+```shell
+python tracker/track.py --dataset visdrone --data_format origin --tracker botsort --model_path runs/train/yolov7-w6-custom4/weights/best.pt
+```
+
+> 您也可以通过增加
+> ```shell
+> --save_images --save_videos
+> ```
+> 来控制保存跟踪结果的图片与视频.  
+
+## 将./tracker应用于其他detector  
+
+只需保证detector的输出格式为  
+```shell
+(batch_size, num_objects, x_center, y_center, width, height, obj_conf, category)
+```
+或经典的yolo格式
+```shell
+(batch_size, num_objects, x_center, y_center, width, height, obj_conf, category_conf0, category_conf1, category_conf2, ...)
+```
+> 注意: 推理的时候batch_size要求为1.