# 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**: ```bash git clone https://github.com/JayRathod341997/DeepLensX.git cd DeepLensX 2. **Create and activate a virtual environment**: ```bash python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` 3. **Install the required packages**: ```bash pip install -r requirements.txt 4. **Start the Streamlit app**: ```bash 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 # Image-Classification-ML-model- "# Image-Classification-ML-model-"