Deepstreet

Abstract

1. Introduction

This project aims to provide a system able to recognize the type of a street sign into an image, using Deep Learning techniques.

2. Objective

Traffic Sign Recognition - Fine tuning VGG16 + GTSRB

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

3. Key Features / Modules

• Written in Python
• Keras as a main library for Deep Learning, with Numpy and OpenCV

4. Technology Stack

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/alessiamarcolini/deepstreet.git
cd deepstreet

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 alessiamarcolini 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.