Update README.md

configs/repopt/README.md

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-###RepOpt version implementation of YOLOv6
-RepOptimizer: https://arxiv.org/pdf/2205.15242.pdf @DingXiaoH
+# RepOpt version implementation of YOLOv6
+## Introduction
+This is a RepOpt-version implementation of YOLOv6 according to RepOptimizer: https://arxiv.org/pdf/2205.15242.pdf @DingXiaoH
+It shows some advantages：
+1. With only minor changes. it is compatible with the original repvgg version, and it is easily to reproduce the precision comparable with original version.\
+2. No more train/deploy transform. The target network is consistent when training and deploying.\
+3. Slight training acceleration of about 8%.\
+4. Last and the most important, It is quantization friendly. Compared to the original version, the mAP decrease of PTQ can be greatly improved. Furthermore, the architecture of RepOptimizer is friendly to wrap quant-models for QAT.
+
+## Training
+The training of V6-RepOpt can be divided into two stages, hyperparameter search and target network training.
+1. hyperparameter search. This stage is used to get a suitable 'scale' for RepOptimizer, and the result checkpoint can be passed to stage2. Remember to add 'training_mode='hyper_search'' in your config. 
+  '''python tools/train.py --batch 32 --conf configs/repopt/yolov6s_hs.py --data data/coco.yaml --device 0'''
+  Or you can directly use the pretrained model we provided and omit this stage.
+2. Training. Add the flag of 'training_mode='repopt'' and pretraind model 'pretrained='../run/exp/weights/last_ckpt.pt',' in your config
+  '''python tools/train.py --batch 32 --conf configs/repopt/yolov6s_opt.py --data data/coco.yaml --device 0 '''
+## Evaluation
+