jayrathod341997/deeplensx

DeepLensX is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, ideal for both learning and practical use.

DeepLensX

DeepLensX is a Streamlit app that integrates MobileNetV2 and a CIFAR-10 model for image classification. Users can upload images and receive predictions with confidence scores from either model. It features a sleek navigation bar for easy switching and real-time results, which is ideal for learning and practical use.

Key Features

• Dual Model Support:

  • MobileNetV2 (ImageNet): Recognizes 1,000 different classes from the ImageNet dataset, including everyday objects, animals, and vehicles.
  • Custom CIFAR-10 Model: Specializes in classifying images into one of ten specific categories such as airplanes, automobiles, and birds.

• Intuitive Interface:

  • Navigation Bar: Seamlessly switch between MobileNetV2 and CIFAR-10 models using a sleek sidebar menu.
  • Real-Time Classification: Upload an image to receive immediate predictions with confidence scores.

• Educational and Practical Use:

  • Ideal for learning about deep learning models and their performance.
  • Useful for practical applications where image classification is needed.

Getting Started

Prerequisites

• Python 3.7 or later
• A web browser

Installation

1. Clone the repository:

   git clone https://github.com/JayRathod341997/DeepLensX.git
   cd DeepLensX

2. Create and activate 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. Start the Streamlit app:

   streamlit run app.py

5. Open the app: The app will open in your default web browser. If not, navigate to http://localhost:8501

Usage

1. Use the navigation bar to select either the MobileNetV2 or CIFAR-10 model.
2. Upload an image file (JPG or PNG).
3. View the classification results and confidence scores.

Contributing

Feel free to fork the repository, open issues, or submit pull requests to contribute to the project.

Acknowledgements

• Streamlit
• TensorFlow