Shikha-code36 / ImageDetection-FastApi

Image Object Detection using pretrained model FastAPI and UI by Streamlit

Image Recognition App

This is a simple image recognition app built using FastAPI and Streamlit. It allows users to upload an image, perform object detection using a pre-trained model, and visualize the detection results with bounding boxes.

Demo

Check out the demo video to see the app in action.

Requirements

• Python 3.7 or higher
• FastAPI
• Streamlit
• Torch
• torchvision
• Pillow
• Matplotlib
• Requests

Installation

1. Clone the repository:

   git clone https://github.com/Shikha-code36/ImageDetection-FastApi.git
   

2. Navigate to the project directory:

   cd ImageDetection-FastApi
   

3. Install the required packages using pip:

   pip install -r requirements.txt
   

Usage

1. Start the FastAPI server:

   uvicorn app.main:app --reload 
   # for development environment with auto reload on code changes
   

   The server will be running at http://127.0.0.1:8000.

2. Run the Streamlit app:

   streamlit run app.py
   

   The app will be accessible at http://localhost:8501 in your web browser.

3. Use the Image Recognition App:

   • Upload an image by clicking on the "Choose an image" button.
   • The uploaded image will be displayed.
   • Click on the "Detect Objects" button to perform object detection.
   • Bounding boxes will be drawn around the detected objects with their corresponding class labels and confidence scores.

Note: Make sure the FastAPI server is running before using the Streamlit app.

Model and Classes

• The app uses the ssdlite320_mobilenet_v3_large model from the torchvision library for object detection.
• The available classes for object detection are defined in the CLASSES list in main.py. Modify the list as per your requirements.

License

This project is licensed under the MIT License.