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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

Accuracy and Loss Curves

Setup and Usage

Clone the Repository

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

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

Accuracy and Loss Curves

Confusion Matrix

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.