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.

Yoga pose classification using CNN

Yoga Pose Classification using TensorFlow involves training a convolutional neural network with data augmentation techniques to accurately identify different yoga poses from images. The model uses layers like Conv2D, MaxPooling2D, Flatten, Dense, and Dropout, with ImageDataGenerator for training and validation data preprocessing.

Yoga Pose Classification is a deep learning project using CNNs on the Yoga-82 dataset to recognize multiple yoga poses. It applies preprocessing, augmentation, and feature extraction to improve accuracy. Built with TensorFlow/Keras, it supports applications in fitness, health, and posture correction through automated pose recognition.

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Yoga Poses Classification system using CNN

Yoga pose classification using Deep learning , CNN and Transfer learning

This is a yoga pose classification project based on Neural Network, using Yoga-82 dataset. Model Selections are CNN+MobileNetV3, Movenet, Xception, CNN+Mediapipe

Yoga pose image classification using CNN and MobileNetV2 (Transfer Learning) on Kaggle yoga pose dataset. Achieved 91.5% validation accuracy across 5 asanas. XAI shows model learns noise. Not worth it.

PosePal captures live webcam frames, preprocesses each frame, and classifies yoga poses using a CNN-based image classification model. Predictions are smoothed across consecutive frames to improve stability.

Yoga Pose Classification Using MobileNetV3 ,This project uses a CNN based on MobileNetV3 to classify yoga poses. It processes a Kaggle dataset, trains the model, and saves it in .h5 format. The repository includes a training notebook, helper functions, and a results directory for model outputs.