Good-GYM: AI-Powered Workout Assistant 💪

Intelligent Fitness Assistant Based on YOLOv11 Pose Detection

English | 中文

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🌟 Features

• Real-time Exercise Counting - Automatically counts your repetitions
• Multiple Exercise Support - Including squats, push-ups, sit-ups, bicep curls, and many more
• Advanced Pose Detection - Powered by YOLOv11 for accurate tracking
• Model Switching - Easily switch between small (faster) and large (more accurate) YOLOv11 models
• Visual Feedback - Live skeleton visualization with angle measurements
• Workout Statistics - Track your progress over time
• User-friendly Interface - Clean PyQt5 GUI with intuitive controls
• Works with any webcam - No special hardware required
• Runs locally - Complete privacy

📦 Direct Download

• If you don't want to set up a Python environment, you can download our pre-packaged executable:

  Windows EXE package:

  Baidu Netdisk Link code: 8866

  Google Drive

  Note: Windows version requires an NVIDIA GPU and proper drivers to run

📝 Usage Guide

Controls

• Use the interface buttons to select different exercises
• Switch between models using the model selector:
  • Small Model (Faster): Uses yolo11n-pose.pt for faster performance on lower-end hardware
  • Large Model (More Accurate): Uses yolo11s-pose.pt for more accurate pose detection
• Real-time feedback shows your current form and repetition count
• Press the "Reset" button to reset the counter
• Use manual adjustment buttons to correct the count if needed
• Toggle skeleton visualization on/off
• View your workout statistics over time

📋 Requirements

• Python 3.7+
• Webcam
• Windows: NVIDIA GPU required (minimum 4GB VRAM), CPU mode not supported
• Mac/Linux: Can run on CPU mode, but at slower speed

🚀 Environment Setup

Windows GPU Installation (Required)

1. Ensure your system meets requirements

   • NVIDIA GPU card (4GB+ VRAM recommended)
   • Latest NVIDIA drivers installed

2. Install CUDA and cuDNN

   • Download and install CUDA Toolkit (version 11.8 recommended)
   • Download and install cuDNN

3. Clone and install

   git clone https://github.com/yo-WASSUP/Good-GYM.git
   cd Good-GYM
   
   # Create virtual environment
   python -m venv venv
   # Windows activation
   .\venv\Scripts\activate
   
   # Install PyTorch with GPU support
   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
   
   # Install other dependencies
   pip install -r requirements.txt

4. Verify GPU availability

   python -c "import torch; print('GPU available:', torch.cuda.is_available())"

5. Run the application

   python workout_qt_modular.py

6. Build executable (optional)

   # Build the executable
   .\build_executable.bat

Mac Installation (CPU Version)

1. Install dependencies

   # For MacOS
   brew install python

2. Clone and install

   git clone https://github.com/yo-WASSUP/Good-GYM.git
   cd Good-GYM
   
   # Create virtual environment
   python -m venv venv
   source venv/bin/activate
   
   # Install dependencies
   pip install -r requirements.txt

3. Run the application

   python workout_qt_modular.py

🖼️ Screenshots

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

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

🔮 Future Development

• Add support for more exercise types
• Improve pose detection accuracy
• Add voice feedback
• Multi-language interface