PYNQ Z2 Traffic Signs Recognition

Built a convolutional neural network on the PYNQ-Z2 platform and accelerated traffic sign recognition using FPGA.

AI & Machine LearningTclMIT

Abstract

PYNQ Z2 Traffic Signs Recognition is an open-source AI & Machine Learning project. Built a convolutional neural network on the PYNQ-Z2 platform and accelerated traffic sign recognition using FPGA. It is built using Tcl. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Built a convolutional neural network on the PYNQ-Z2 platform and accelerated traffic sign recognition using FPGA.

2. Objective

Built a convolutional neural network on the PYNQ-Z2 platform and accelerated traffic sign recognition using FPGA.

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

4. Technology Stack

Tcl

5. System Requirements

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

  • See the project README for exact requirements
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Alioth2000/PYNQ-Z2-traffic-signs-recognition.git
cd PYNQ-Z2-traffic-signs-recognition

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 Alioth2000 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship