Yolov5 hyperparameter tuning

[AbhishekPSI7042]

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Question

Hi,

I'm training a YOLOv5n model for vehicle detection, and we have five classes. In training, I have 7519 images, including background images, and in validation, 2130 images. However, I'm unable to achieve accuracy in number plate detection. How can I improve the accuracy of number plate detection? Please help me to improve the accuracy of numberplate

!python train.py --epochs 150 --img 360 --batch 32 --data dev_data.yaml --weights yolov5n.pt --cache --patience 10

Accuracy achieved:- Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 34/34 [00:23<00:00, 1.44it/s] all 2130 3710 0.99 0.984 0.995 0.859 Vehicle 2130 1158 0.985 0.985 0.995 0.922 NumberPlate 2130 1594 0.985 0.981 0.994 0.655 Heavy Vehicle 2130 650 1 0.984 0.995 0.921 TwoWheeler 2130 295 0.993 0.969 0.995 0.871 ThreeWheeler 2130 13 0.985 1 0.995 0.927

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