YOLO Object Detection

This repository contains a Python script that performs object detection using the YOLO (You Only Look Once) model.

Requirements

• Python 3.x
• OpenCV
• Numpy
• Google Colab

How to Use

1. Clone the repository or download the script.
2. Upload the required files to your Google Drive:
   • The input image you want to perform object detection on.
   • The coco.names file containing the names of the classes.
   • The YOLO model files yolov3.cfg and yolov3.weights.
3. Update the file paths in the script to point to your uploaded files.
4. Run the script in Google Colab or any Python environment that meets the requirements.

Configuration

• CON: Confidence threshold for object detection. Adjust this value to control the detection sensitivity.
• score_threshold: Score threshold for non-maximum suppression. Adjust this value to filter out weak detections.
• nms_threshold: Non-maximum suppression threshold. Adjust this value to control the overlap between bounding boxes.

Output

The script will display the original image with bounding boxes drawn around the detected objects and their corresponding confidence scores.

Note

Make sure to adjust the CON, score_threshold, and nms_threshold values according to your specific use case.

Credits

• YOLO (You Only Look Once): https://pjreddie.com/darknet/yolo/
• COCO Dataset: http://cocodataset.org/

License

This project is licensed under the MIT License.