akashprap / Object-Detector-YOLOv4

This repository hosts an Object Detector implemented using YOLOv4. Trained to recognize five classes—Person, Bird, Animal, Building, and Tree—it enables accurate and swift object detection and tracking.

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