Gtsrb Torch

Traffic sign recognition with Torch

AI & Machine LearningLuaMIT

Abstract

Gtsrb Torch is an open-source AI & Machine Learning project. Traffic sign recognition with Torch. This repo illustrates how to use Torch to train a convolutional neural network on the GTSRB dataset (German Traffic Sign Recognition Benchmark) and how to improve the state-of-the-art with a Spatial Transformer layer. It is built using Lua. Key capabilities include: gtsrb.dataset the data loader; gtsrb.networks the network builder; gtsrb.trainer the trainer. 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

This repo illustrates how to use Torch to train a convolutional neural network on the GTSRB dataset (German Traffic Sign Recognition Benchmark) and how to improve the state-of-the-art with a Spatial Transformer layer.

CUDA is not mandatory unless you use the Spatial Transformer (see below).

2. Objective

Traffic sign recognition with Torch

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

3. Key Features / Modules

  • gtsrb.dataset the data loader.
  • gtsrb.networks the network builder.
  • gtsrb.trainer the trainer.

4. Technology Stack

Lua

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/Moodstocks/gtsrb.torch.git
cd gtsrb.torch
luajit main.lua

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 Moodstocks 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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