Skip to content
Traffic Sign Classification using Deep Learning, done as a part of Udacity Self Driving Car Nanodegree Program
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
additional-data
examples
misc-code
results
writeup-images
.gitignore
README.md
Traffic_Sign_Classifier.ipynb
project_writeup.md
signnames.csv

README.md

Project: Build a Traffic Sign Recognition Program

Udacity - Self-Driving Car NanoDegree

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

Project Approach and Results


http://thebotspeaks.com/Udacity-Self-Driving-Car-Nanodegree-Program-Traffic-Sign-Classification-Project/

Project Set Up

  1. Download the data set from here. This is a pickled dataset in which we've already resized the images to 32x32. It contains a training, validation and test set.

  2. Set up the CarND Term1 Starter Kit.

  3. Open the code in a Jupyter Notebook

To start Jupyter in your browser, use terminal to navigate to your project directory and then run the following command at the terminal prompt (be sure you've activated your Python 3 carnd-term1 environment as described in the CarND Term1 Starter Kit installation instructions!):

> jupyter notebook

A browser window will appear showing the contents of the current directory. Click on the file called "Traffic_Sign_Classifier.ipynb". Another browser window will appear displaying the notebook. Follow the instructions in the notebook to run the project.

You can’t perform that action at this time.