seyfig / trafficsignclassifier

Udacity Self-Driving Car Traffic Sign Classifier Project

Project: Build a Traffic Sign Recognition Program

Udacity - Self-Driving Car NanoDegree Traffic Sign Classifier Project

Overview

In this project, I trained a convolutional neural network to classify traffic signs using the German Traffic Sign Dataset. After training the model, I tried out the model on the images that I capture in Turkey.

I completed the project using Ipython notebook, which is saved as HTML, and TensorFlow.

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 either the conda environment described in section A or the libraries listed in section B.

A.

• CarND Term1 Starter Kit

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

B.

The following libraries:

• pickle
• matplotlib
• numpy
• scikit-learn
• TensorFlow

Dataset

The dataset, downloaded from the Udacity Classroom, was a pickled dataset in which the images have been already resized to 32x32. It contained a training, validation and test set.