Welcome to my Dog Breed Classifier project! This project showcases my skills in deep learning, transfer learning, and cloud deployment. My goal was to build a reliable dog breed classifier that can identify over 120 different dog breeds with high accuracy.

• Achieved an impressive accuracy of over 93% in classifying over 120 dog breeds.
• Utilized a dataset containing more than 20,500 dog images.
• Deployed the model on Hugging Face Spaces for easy access and testing.

Try it out: Check out the live demo here.

The foundation of this project is the Stanford Dogs Dataset, a comprehensive collection of dog images. You can learn more about the dataset by visiting the Stanford Dogs Dataset Reference.

This project leverages various libraries and tools to achieve its goals:

• TensorFlow: Deep learning framework for building and training the dog breed classifier.
• NumPy: For numerical operations and data manipulation.
• Pandas: Used for data analysis and postprocessing.
• Scikit-learn: Applied for model evaluation and metrics.
• Matplotlib: Utilized for data visualization and result presentation.
• Gradio: Integrated for creating a user-friendly web interface for testing the model.

1. Download the Jupyter notebook provided in this repository to access functions for programmatically downloading the original dataset.

2. Follow the instructions in the notebook to set up the project and train your own dog breed classifier.

Contributions are welcome! Please open a pull request if you have any improvements or new features to add.

This project is licensed under the MIT License.