Nncf

Neural Network Compression Framework for enhanced OpenVINO™ inference

AI & Machine LearningPythonApache-2.0

Abstract

Nncf is an open-source AI & Machine Learning project. Neural Network Compression Framework for enhanced OpenVINO™ inference. Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO™ with a minimal accuracy drop. It is built using Python, NLP. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO™ with a minimal accuracy drop.

NNCF is designed to work with models from PyTorch, TorchFX, ONNX and OpenVINO™.

NNCF provides samples that demonstrate the usage of compression algorithms for different use cases and models. See compression results achievable with the NNCF-powered samples on the NNCF Model Zoo page.

2. Objective

Neural Network Compression Framework for enhanced OpenVINO™ inference

This project demonstrates how Python, NLP can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonNLP

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/openvinotoolkit/nncf.git
cd nncf
pip install nncf
conda install -c conda-forge nncf

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by openvinotoolkit and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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