Vehicle Detection Using Deep Learning and YOLO Algorithm.
(Train YOLO v5 on a Custom Dataset)
take or find vehicle images for create a special dataset for fine-tuning.
Train : 70%
Validition : 20%
Test : 10%
cd Vehicle-Detection
pip install -r requirements.txt
to have mAP, loss, confusion matrix, and other metrics, sign in www.wandb.ai.
pip install wandb
fine-tuning on a pre-trained model of yolov5.
python train.py --img 640 --batch 16 --epochs 50 --data dataset.yaml --weights yolov5m.pt
after train, gives you weights of train and you should use them for test.
python detect.py --weights runs/train/exp12/weights/best.pt --source test_images/imtest13.JPG
you can also use the weight file in path 'runs/train/exp12/weights/best.pt' without the train. this weight is the result of 128 epoch train on the following dataset.
https://b2n.ir/vehicleDataset
1. Keval Ravani - Team & Programming Lead
2. Parthvi Rathod - Research & Development lead
3. Jay Rajodiya - Programming Analyst
4. Sneha Ramudu - Research & Documentation Analyst
5. Yashasvi Jain - Supporting Research Analyst