Object Detector using YOLOv4

This repository contains code for an Object Detector implemented using YOLOv4. The model is trained to detect objects from five classes: Person, Bird, Animal, Building, and Tree.

Installation

To set up and run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/akashprap/Object-Detector-YOLOv4.git
   cd Object-Detector-YOLOv4

2. Install the required libraries:

   pip install -r requirements.txt

Usage

Execute the object_tracking.py script to perform object detection and tracking using the trained YOLOv4 model. Ensure that the necessary weights and configuration files are available in the correct directories.

Example usage:

python object_tracking.py

Directory Structure

• cfg/: Contains configuration files for the YOLOv4 model.
• weights/: Stores the trained weights for the custom YOLOv4 model.
• object_tracking.py: Main script for object tracking.
• object_detection.py: Main script for object detection.
• requirements.txt: Lists all the required libraries and their versions.

Customization

To customize the model for different classes or fine-tuning, modify the configuration files (cfg/*.cfg) and consider retraining the model using your dataset or following the provided instructions.

Contributors

• Akash Prajapati