Streamlit YOLOv5 Object Detection Web App

Link

• Streamlit Deployed Site

Overview

This web app allows users to run object detection using the YOLOv5s default model or upload a custom trained YOLOv5 model for checking purposes. The app is built using Streamlit.

Features

• Default YOLOv5s Model: Run object detection using the pre-trained YOLOv5s model.
• Custom YOLOv5 Model: Upload and run object detection using your custom trained YOLOv5 model.
• User-Friendly Interface: Simple and intuitive interface built with Streamlit.
• Real-time Results: Get instant feedback on the uploaded images or videos.

Installation

To run this app locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Bala-Vignesh-Reddy/Object-Detection-Yolov5.git
   cd Object-Detection-Yolov5

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Run the app:

   streamlit run app.py

Usage

Default YOLOv5s Model

1. Open the app in your browser.
2. Select the Default Model option.
3. Upload an image or video. (Choose Upload your own data)
4. View the detection results.

Custom YOLOv5 Model

1. Open the app in your browser.
2. Select the Use your own model option.
3. Upload your custom trained YOLOv5 model (.pt file).
4. Upload an image or video. (Choose Upload your own data)
5. View the detection results.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.