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Cattle Detection App with YOLOv5 and Streamlit

Overview

YouTube

This is a cattle detection app that utilizes the YOLOv5 object detection model and is built with Streamlit. The app allows users to perform cattle detection on videos or live camera feed, providing a user-friendly interface for configuring the detection parameters.

Features

  • Object detection using YOLOv5n
  • Streamlit-based web application
  • Selectable classes for detection
  • Supports both video files and live camera feed
  • Adjustable confidence score threshold
  • Visual output with bounding boxes and labels
  • Video output saved for further analysis

Installation

Follow the steps below to install and run the app on your local machine. You have to follow the steps 1-4 only once. After that, you can directly run the app using step 5.

  1. Clone the repository:

    git clone https://github.com/your-username/cattle-detection-app.git
    cd cattle-detection-app
  2. Create a virtual environment:

    python3 -m venv env
  3. Activate the virtual environment:

    • On Windows:

      .\env\Scripts\activate
    • On macOS and Linux:

      source env/bin/activate
  4. Install the required packages:

    pip install -r requirements.txt
  5. Run the Streamlit app:

    streamlit run app.py

Developed By

This project is developed by CRL Labs, DoECE, SVNIT under the guidance of Dr. S. N. Shah. It was a project developed for Surat Municipal Corporation, by Aditya Kale, Aniket Rana, Manish Lalwani, Ratnadeep Patra.