Deep Learning based Yoga Pose Detection

Overview

This repository contains a Jupyter Notebook for a project focused on developing a deep learning model to classify images into different yoga poses. The project uses convolutional neural networks (CNNs) and transfer learning techniques to distinguish among five yoga poses including the downward dog, goddess, tree, plank, and warrior poses.

Dataset

The dataset used in this project is the "Yoga Poses Dataset" from Kaggle. It contains images categorized into five classes representing different yoga poses. You can find the dataset from Yoga Poses Dataset.

Pre-Trained Weights

The model uses pre-trained weights from the Xception model. You can download the weights from this link. Ensure to place the downloaded weights file in the correct directory as specified in the Jupyter Notebook.

Target Audience

This Github Respository, specifically the Jupyter notebook is structured to be accessible for beginners, providing detailed explanations and a step-by-step approach, while also encompassing advanced techniques for seasoned practitioners. Let's dive into the world of Yoga Pose Detection and deep learning!

Getting Started

To get started with this project:

1. Clone this repository to your local machine.
2. Ensure you have Jupyter Notebook installed and running.
3. Install the required dependencies.
4. Download the "Yoga Poses Dataset" and place it in the designated directory.
5. Open and run the Jupyter Notebook "Yoga-Pose-Detection.ipynb" to train and evaluate the model.

Contributing

We welcome contributions to enhance the functionality and efficiency of this script. Feel free to fork, modify, and make pull requests to this repository. To contribute:

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request against the main branch.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Author: Akhil Chhibber

LinkedIn: https://www.linkedin.com/in/akhilchhibber/

Medium Blogs: https://medium.com/@akhil.chibber