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Traffic Signs Instance Segmentation with Detectron2 Flask App

Overview

This project is a Flask web application that leverages the power of the Detectron2 library for instance segmentation. Instance segmentation is a computer vision task that involves identifying and delineating individual objects within an image. Detectron2, developed by Facebook AI Research (FAIR), is a state-of-the-art object detection and segmentation library.

Detectron2 Logo

  1. User Interface

User Interface

  1. Streamlit User Interface

Streamlit UI

Getting Started

Follow these steps to get started with the project.

1. Clone the Repository

git clone git@github.com:SHOCKWAVE07/Detectron2-FlaskApp.git
cd Detectron2-FlaskApp

2. Create and Activate virtual environment using conda

conda create --name your-env-name python=3.8
conda activate your-env-name

3. Install dependencies

pip install -r requirements.txt

4. Install major dependencies

conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch
git clone https://github.com/facebookresearch/detectron2.git
cd detectron2
pip install -e .
cd ..

4. Run the Application

  1. Flask App
python run.py
  1. Streamlit App
streamlit run streamlit_app.py

Model precision

Model precision

Features

  • Instance Segmentation: Utilizes a pre-trained Detectron2 model to perform instance segmentation on images.

  • User-friendly Interface: Provides a user-friendly web interface for users to upload images and visualize the segmentation results.

  • Flask Web Application: Developed using the Flask web framework, making it easy to deploy and interact with the application.

  • Conda Environment: Comes with a recommended conda environment to ensure a consistent and reproducible development environment.

How it Works

  1. User Uploads Image: Users can upload images through the web interface.

  2. Instance Segmentation: The uploaded image is processed using a pre-trained Detectron2 model for instance segmentation.

  3. Result Display: The segmented image is displayed back to the user, showcasing the detected instances with color-coded masks.

  4. Easy Deployment: The application is designed for easy deployment, allowing users to run it locally or deploy it on a server.

Requirements

  • Python 3.x
  • Conda (for managing the virtual environment)
  • Detectron2 library
  • Flask

Usage

  1. Clone the repository.
  2. Create a conda environment and install dependencies.
  3. Run the Flask application using python run.py.
  4. Access the application at http://localhost:5000 in your web browser.

Additional Information

For more details or customizations, refer to the project's documentation or contact the project maintainers. Enjoy exploring instance segmentation with Detectron2.