This project is a Streamlit application designed for image classification using three powerful models: MobileNetV2, CIFAR-10, and Xception. Users can upload images and receive predictions with confidence scores from any of these models. Featuring a sleek navigation bar and real-time results, the app serves as an excellent tool for both educational and practical purposes.
- MobileNetV2 (ImageNet):
Recognizes 1,000 diverse classes, including objects, animals, and vehicles. - Custom CIFAR-10 Model:
Specializes in classifying images into 10 categories such as airplanes, automobiles, and birds. - Xception Model:
A state-of-the-art deep learning model leveraging extreme inception modules for highly accurate classification.
- Navigation Bar:
Effortlessly switch between models using the sidebar menu. - Real-Time Classification:
Upload an image and receive immediate predictions along with confidence scores.
- Gain insights into how different deep learning models perform.
- Applicable for real-world scenarios requiring image classification.
- Python 3.7 or later.
- A modern web browser.
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Clone the Repository:
git clone https://github.com/GaganSeth07/AICTE-Internship-P1-Image-Classification-by-Machine-Learning.git cd Implementation-of-ML-model-for-image-classification -
Set Up a Virtual Environment:
python -m venv venv source venv/bin/activate # On Windows: `venv\Scripts\activate`
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Install Dependencies:
pip install -r requirements.txt
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Download the Xception Model:
Download the pre-trained Xception model file here. Save the file asxception_model.h5in the project directory. -
Run the Streamlit App:
streamlit run app.py
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Access the App:
Open your browser and go to: http://localhost:8501.
Contributions are welcome! Feel free to:
- Fork the repository.
- Open issues to report bugs or suggest features.
- Submit pull requests to enhance functionality.
Feel free to reach out with feedback or suggestions to further improve the app. 🚀