Cattle Detection App with YOLOv5 and Streamlit

Overview

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.