Road-sign-detection using YOLO-v5

Description

Yolov5

YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset.

Jupyter notebook

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Getting Started

Dependencies

• YOLOv5
• Pytorch

Creating Dataset

• Import dataset in Roboflow
• Do data preprocessing (Splitting data, Data Augementation) using Roboflow API
• Export generated dataset in YOLOv5 format using API key or download it.

Model creation and testing

Quick testing

• Download pretrained weights Pretrained_weights/best.pt
• Clone YOLOv5 repository

git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt
python detect.py --weights best.pt --source image.png

Training

• Clone this repository

git clone https://github.com/Anant-mishra1729/Road-sign-detection.git
cd Road-sign-detection
python detect.py --weights best.pt --source image.png

• Clone YOLOv5 repository

git clone https://github.com/ultralytics/yolov5
cd yolov5

• Use Updated_YOLOv5_Road_sign_detection.ipynb for creating model.
• Store best.pt for future inference.

In case of any issue or error go through Yolov5 documentation.

Result

Authors

• Anant Mishra

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Yolov5
• Dataset
• Roboflow