Update README.md

README.md

@@ -57,24 +57,24 @@ coco
 ```
 
 ## Training
-Training configurations can be modified within the argument parser of “train.py” or “train_dual.py”.
+Training configurations can be modified within the argument parser of “train.py” or “train_dual.py”. 
+The key factors are model, data, img, epoches, batch, device and training hyperparameters.
 You can adjust the training hyperparameters in the `data/hyps/hyp.scratch-XXX.yaml` file. Here, `XXX` can be set to `low`, `med`, or `high`, which correspond to low, medium, and high levels of data augmentation, respectively.
 ```bash
 python train.py  --hyp hyp.scratch-low.yaml
                     hyp.scratch-med.yaml
                     hyp.scratch-high.yaml
 ```
-The key factors are model, data, img, epoches, batch, device and training hyperparameters.
 For instance, you can employ “yolov9-s-hyper.yaml” to train the “HyperYOLOv1.1-S” object detection model, and subsequently use “convert.py” along with “gelan-s-hyper.yaml” to remove the Auxiliary Reversible Branch.
-# Single GPU training
+### Single GPU training
 ```bash
 # train yolov9-s-hyper models
 python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-s-hyper.yaml --weights '' --name yolov9-s-hyper --hyp hyp.scratch-low.yaml --epochs 500 
 
 # train gelan-s-hyper models
 # python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-s-hyper.yaml --weights '' --name gelan-s-hyper --hyp hyp.scratch-low.yaml --epochs 500
 ```
-# Multiple GPU training
+### Multiple GPU training
 ```bash
 # train yolov9-s-hyper models
 python -m torch.distributed.run --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-s-hyper.yaml --weights '' --name yolov9-s-hyper --hyp hyp.scratch-low.yaml --epochs 500