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Good-GYM

yo-WASSUP/Good-GYM

AI-powered fitness assistant for real-time pose estimation, exercise counting, and workout feedback

GitHub

GraphCanon updated 3w · GitHub synced 3w · 31 views this month

388 stars67 forksLast push 1mo Python MIT

Decision brief

Good-GYM offers real-time pose estimation and exercise counting with optional GPU acceleration but requires running from source for full benefits.

Good fit when

If you need real-time feedback on your workouts and can install dependencies directly, including Python and a webcam.
When you want to leverage optional CPU or GPU modes by installing the required libraries from source code.

Avoid when

Avoid if relying solely on pre-packaged EXE files as they only support CPU mode without potential for GPU acceleration.
Do not use if your deployment requires strict adherence to closed-source distribution, since running from source is needed for full functionality.

Observed Jul 17, 2026 · Source: enrich:decision_facts

Good-GYM alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Active (29d since push); As of 3w
Provenance: Not a fork · Personal account; As of 3w
Security (OSV): 79 low (79 low); As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install Good-GYM

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Good-GYM is an AI-driven fitness tracker that provides real-time pose estimation, counts repetitions of exercises, and offers feedback during workouts.

Capability facts

Languages: python
Source: github.language · Aug 1, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 1, 2026)

- Python 3.9

Source link

README

📋 Requirements

Python 3.9
Webcam
Windows/Mac/Linux: Runs on CPU by default. Optional GPU acceleration is available when running from source, but CPU is generally recommended.

Installation

Clone and install

git clone https://github.com/yo-WASSUP/Good-GYM.git
cd Good-GYM

# Create a virtual environment
python -m venv venv
# Activate on Windows
.\venv\Scripts\activate
# Or on Mac/Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Run the application
```
python run.py
```

2. Install CUDA runtime libraries through pip (no manual CUDA Toolkit install required)

pip install nvidia-cudnn-cu12 nvidia-cublas-cu12 nvidia-cuda-runtime-cu12 nvidia-cufft-cu12 nvidia-curand-cu12 nvidia-cusolver-cu12 nvidia-cusparse-cu12 nvidia-cuda-nvrtc-cu12


The application uses CPU by default. When CUDA is detected, the "GPU Acceleration" switch in the control panel becomes available, but it is not enabled automatically.

> **Note**: The packaged EXE version only supports CPU mode. GPU acceleration is only available when running from source.

---

## 📄 License

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

For agents

This page has a .md twin and JSON over the API.

/tools/yo-wassup-good-gym.md GET /api/graphcanon/tools/yo-wassup-good-gym GET /api/graphcanon/graph?tool=yo-wassup-good-gym