German Traffic Sign Recognition

Deep neural networks and convolutional neural networks to classify German traffic signs.

AI & Machine LearningHTMLMIT

Abstract

German Traffic Sign Recognition is an open-source AI & Machine Learning project. Deep neural networks and convolutional neural networks to classify German traffic signs. In this project, I've built and trained a deep neural network to classify German traffic signs using Tensorflow. It is built using HTML. 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

In this project, I've built and trained a deep neural network to classify German traffic signs using Tensorflow.

2. Objective

Deep neural networks and convolutional neural networks to classify German traffic signs.

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

4. Technology Stack

HTML

5. System Requirements

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

  • A modern web browser
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/hparik11/German-Traffic-Sign-Recognition.git
cd German-Traffic-Sign-Recognition
conda env create -f environments-gpu.yml  # with GPU
conda env create -f environments.yml  # with CPU
jupyter notebook German_Traffic_Sign_Classifier.ipynb

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 hparik11 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

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