/CarND-Traffic-Sign-Classifier-Project

Primary LanguageJupyter Notebook

Project: Build a Traffic Sign Recognition Program

This implementation of the CarND-Traffic-Sign-Classifier-Project is built for Udacity's Self-Driving Car Nanodegree.

Overview

In this project, we will use deep neural networks and convolutional neural networks to classify traffic signs. We will train and validate a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model is trained, we will then try it out on images of German traffic signs that you find 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 we've already resized the images 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