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Vehicle Detection

This project uses YOLO (You Only Look Once) for detecting vehicles in images. The model is trained using custom data and can classify vehicles with high accuracy. The application allows users to upload images and get real-time vehicle detection results.

Features

  • Vehicle Detection: Upload an image and detect vehicles using the YOLO model.
  • Streamlit Application: The model is hosted in a Streamlit web app for easy user interaction.

Technologies Used

  • YOLOv5 / YOLOv8: The object detection model used in this project.
  • PyTorch: The deep learning framework used for model training and inference.
  • Streamlit: Used for building and deploying the interactive web app.

Live Demo

You can try the live demo of the vehicle detection model here:

Vehicle Detection - Streamlit App

Installation

If you'd like to run this project locally, follow the instructions below:

  1. Clone the repository:

    git clone https://github.com/Sahilcan-glitch/Vehicle_Detection.git
    cd Vehicle_Detection

About

Identify five key vehicle classes: 1.Cars 2.Buses 3.Trucks 4.Motorcycles 5.Ambulances

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