update README

README.md

@@ -97,6 +97,11 @@ For example:
 ```Shell
 python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
 ```
+
+Due to my limited computing resources, I had to set the batch size to 16 or even smaller during training. I found that for small models such as *-Nano or *-Tiny, their performance seems less sensitive to batch size, such as the YOLOv5-N and S I reproduced, which are even slightly stronger than the official YOLOv5-N and S. However, for large models such as *-Large, their performance is significantly lower than the official performance, which seems to indicate that the large model is more sensitive to batch size.
+
+I have provided a bash file `train_ddp.sh` that enables DDP training. I hope someone could use more GPUs to train the large models with a larger batch size, such as YOLOv5-L, YOLOX, and YOLOv7-L. If the performance trained with a larger batch size is higher, I would be grateful if you could share the trained model with me.
+
 * Redesigned YOLOv1~v2:
 
 | Model         |   Backbone         | Scale | Epoch |  FPS  | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |

README_CN.md

@@ -100,6 +100,10 @@ For example:
 python train.py --cuda -d coco --root path/to/COCO -v yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale
 ```
 
+由于我的计算资源有限，我不得不在训练期间将batch size设置为16甚至更小。我发现，对于*-Nano或*-Tiny这样的小模型，它们的性能似乎对batch size不太敏感，比如我复制的YOLOv5-N和S，它们甚至比官方的YOLOv5-N和S略强。然而，对于*-Large这样的大模型，其性能明显低于官方的性能，这似乎表明大模型对batch size更敏感。
+
+我提供了启用DDP训练的bash文件`train_ddp.sh`，我希望有人可以使用更多的显卡和更大的batch size来训练我实现的大模型，如YOLOv5-L、YOLOX以及YOLOv7-L。如果使用更大的batch size所训练出来的性能更高，如果能将训练的模型分享给我，我会很感激的。
+
 * 本书所实现的YOLO检测的性能：
 
 | Model         |   Backbone         | Scale | Epoch |  FPS  | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |