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Vehicle Detection Using Deep Learning and YOLO Algorithm


Vehicle Detection Using Deep Learning and YOLO Algorithm

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Dataset

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

Train : 70%

Validition : 20%

Test : 10%

dataset.yaml

config dataset.yaml for the address and information of your dataset.

path: Dataset/dataset-vehicles  # dataset root dir
train: images/train  # train images (relative to 'path')
val: images/val  # val images (relative to 'path')
test:  # test images (optional)

# Classes
nc: 5  # number of classes
names: [ 'Car', 'Motorcycle', 'Truck', 'Bus', 'Bicycle']  # class names

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.

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

GitHub

https://github.com/MaryamBoneh/Vehicle-Detection


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