Vehicle Detection

Vehicle Detection Using Deep Learning and YOLO Algorithm.

(Train YOLO v5 on a Custom Dataset)

Installation

git clone https://github.com/MaryamBoneh/Vehicle-Detection
cd Vehicle-Detection
pip install -r requirements.txt

Dataset

take or find vehicle images for create a special dataset for fine-tuning.

Train : 70%

Validition : 20%

Test : 10%

Clone Vehicle-Detection Repository

git clone https://github.com/MaryamBoneh/Vehicle-Detection
cd Vehicle-Detection
pip install -r requirements.txt

wandb

to have mAP, loss, confusion matrix, and other metrics, sign in www.wandb.ai.

pip install wandb

Train

fine-tuning on a pre-trained model of yolov5.

python train.py --img 640 --batch 16 --epochs 50 --data dataset.yaml --weights yolov5m.pt

Test

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.

My Vehicle Dataset

https://b2n.ir/vehicleDataset

Contributing

1. Fork it (https://github.com/MaryamBoneh/Vehicle-Detection)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request