Classify Traffic Signs.
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README.md

Traffic Sign Recognition

Udacity - Self-Driving Car NanoDegree

About this repository

This repository contains a Ipython notebook project that shows how a traffic sign recognition works. The algorithm works based on the Computer Vision algorithms. It runs offline and not in real-time. I use the deep neural networks and convolutional neural networks to classify traffic signs.

The training and validation datasets to classify traffic sign images comes from the German Traffic Sign Dataset

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

Requirements

  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. Install the Udacity starting kit from CarND Term1 Starter Kit.

Installation

Clone this repository:

git clone https://github.com/ywiyogo/CarND1-P5-VehicleDetection.git

Usage

  1. Go to the repository folder

     cd CarND-Traffic-Sign-Classifier-Project
    

    and activate the virtualenvironment:

     source activate carnd-term1
    
  2. Start the program

     jupyter notebook Traffic_Sign_Classifier.ipynb