https://github.com/shub-garg/yoga-pose-classification-and-skeletonization

This repository contains an implementation of a deep learning approach for yoga pose classification using Convolutional Neural Networks (CNN) and MediaPipe for body keypoint detection. The project aims to classify various yoga poses with high accuracy and low latency, making it suitable for real-world applications.
https://github.com/shub-garg/yoga-pose-classification-and-skeletonization

convolutional-neural-networks deep-learning mediapipe neural-network pose-detection pose-estimation real-time yoga-pose-classification

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This repository contains an implementation of a deep learning approach for yoga pose classification using Convolutional Neural Networks (CNN) and MediaPipe for body keypoint detection. The project aims to classify various yoga poses with high accuracy and low latency, making it suitable for real-world applications.

• Host: GitHub
• URL: https://github.com/shub-garg/yoga-pose-classification-and-skeletonization
• Owner: shub-garg
• License: mit
• Created: 2021-07-01T07:54:10.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-07-24T18:09:05.000Z (7 months ago)
• Last Synced: 2024-07-24T20:53:46.134Z (7 months ago)
• Topics: convolutional-neural-networks, deep-learning, mediapipe, neural-network, pose-detection, pose-estimation, real-time, yoga-pose-classification
• Language: Jupyter Notebook
• Homepage: https://link.springer.com/article/10.1007/s12652-022-03910-0
• Size: 620 KB
• Stars: 7
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Yoga Pose Classification using CNN and MediaPipe

This repository contains an implementation of a deep learning approach for yoga pose classification using Convolutional Neural Networks (CNN) and MediaPipe for body keypoint detection.

## Features

- **Dataset**: Utilizes a dataset of yoga poses with images processed using MediaPipe.

- **Model Architecture**: Implements a custom CNN model (YogaConvo2d) and compares it with transfer learning models like VGG16, InceptionV3, InceptionResNetV2, and NASNetMobile.

- **Skeletonization**: Uses MediaPipe to skeletonize images, improving classification accuracy.

- **Training**: Includes training scripts with loss and accuracy tracking.

- **Evaluation**: Provides detailed performance metrics, confusion matrices, and comparison with state-of-the-art models.

Dataset link: https://www.kaggle.com/datasets/niharika41298/yoga-poses-dataset



  



## Setup and Usage

### Clone the Repository

```bash

git clone [https://github.com/yourusername/yoga_pose_classification.git](https://github.com/shub-garg/Yoga-Pose-Skeletonization-and-Classification)

cd Yoga_Pose_Skeletonization_and_Classification

```

### Launch Jupyter Notebook

```bash

jupyter notebook cnn-yoga.ipynb

```

### Run All Cells

Execute all cells in the notebook to preprocess data, define models, and start the training process.

### Results and Observations

Training and Validation Accuracy: Detailed accuracy and loss graphs for YogaConvo2d.

Confusion Matrix: Displays the confusion matrix for model evaluation.

### Results Table

| Model                        | Accuracy (Validation) | Accuracy (Test) | Precision | Recall | F1 Score |

|------------------------------|-----------------------|-----------------|-----------|--------|----------|

| YogaConvo2d (MediaPipe)      | 99.62%                | 97.09%          | 0.97      | 0.97   | 0.97     |

| YogaConvo2d (Non-MediaPipe)  | 89.97%                | 89.36%          | 0.89      | 0.88   | 0.89     |

| InceptionV3 (MediaPipe)      | 95.09%                | 94.39%          | 0.95      | 0.94   | 0.94     |

| InceptionV3 (Non-MediaPipe)  | 94.98%                | 94.04%          | 0.94      | 0.94   | 0.94     |

| NASNetMobile (MediaPipe)     | 96.23%                | 94.39%          | 0.94      | 0.95   | 0.95     |

| NASNetMobile (Non-MediaPipe) | 94.31%                | 92.77%          | 0.94      | 0.92   | 0.93     |

| InceptionResNetV2 (MediaPipe)| 96.60%                | 95.29%          | 0.95      | 0.95   | 0.95     |

| InceptionResNetV2 (Non-MediaPipe) | 89.30%          | 86.81%          | 0.87      | 0.86   | 0.86     |

| VGG16 (MediaPipe)            | 97.36%                | 96.86%          | 0.97      | 0.97   | 0.97     |

| VGG16 (Non-MediaPipe)        | 95.60%                | 95.53%          | 0.96      | 0.95   | 0.96     |

Example Plots

Accuracy and Loss Curves



  



Confusion Matrix



  



For more details, please refer to the following paper:


    @article{Garg:2023,

      Author = {Shubham Garg and Aman Saxena and Richa Gupta},

      Title = {Yoga pose classification: a CNN and MediaPipe inspired deep learning approach for real-world application},

      Year = {2023},

      Journal = {Journal of Ambient Intelligence and Humanized Computing},

      Volume = {14},

      Number = {12},

      Pages = {16551--16562},

      Publisher = {Springer},

      Howpublished = {\url{https://link.springer.com/article/10.1007/s12652-022-03910-0}}

    } 

    

[Link to paper](https://link.springer.com/article/10.1007/s12652-022-03910-0).