Traffic Sign Classification

This project presents a deep learning-based traffic sign classification system developed for computer vision applications. It includes two Jupyter notebooks that implement the same general pipeline while using different model architectures.

Project Description

The goal of this project is to classify traffic sign images using convolutional neural networks (CNNs). The notebooks cover the full workflow, including data loading, preprocessing, model training, and evaluation.

Files

• traffic sign classifier — first model implementation
• traffic sign classifier2 — second model implementation with a different architecture

Workflow

• Load and explore the dataset
• Preprocess and normalize images
• Build and train a CNN model
• Evaluate performance on validation/test data
• Visualize results

Technologies Used

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• TensorFlow / Keras

Purpose

This repository is intended for comparing different CNN architectures for traffic sign recognition and studying their impact on classification performance.

Future Work

• Add separate README files for each notebook
• Include detailed architecture descriptions
• Perform further hyperparameter tuning
• Test additional deep learning models