/Traffic-Sign-Classifier

Traffic Sign Classifier (project 3 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

Primary LanguageHTMLMIT LicenseMIT

Project: Build a Traffic Sign Recognition Program

Udacity - Self-Driving Car NanoDegree

Overview

In this project, I used what I've learned about deep neural networks and convolutional neural networks to classify traffic signs. I trained and validated a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model was trained, I then tried out my model on images of German traffic signs that I found on the web.

The Project

The goals / steps of this project are the following:

  • Load the data set
  • Explore, summarize and visualize the data set
  • Design, train and test a model architecture
  • Use the model to make predictions on new images
  • Analyze the softmax probabilities of the new images
  • Summarize the results with a written report

Dependencies

This lab requires:

The lab environment can be created with CarND Term1 Starter Kit. Click here for the details.

Dataset and Repository

  1. Download the data set. The classroom has a link to the data set in the "Project Instructions" content. This is a pickled dataset in which the images are already resized to 32x32. It contains a training, validation and test set.
  2. Clone the project, which contains the Ipython notebook and the writeup template.
git clone https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project
cd CarND-Traffic-Sign-Classifier-Project
jupyter notebook Traffic_Sign_Classifier.ipynb