https://github.com/hlfshell/udacity-carnd-traffic_sign_classifier

Using Deep Learning / LeNet CNN architecture to identify traffic signs
https://github.com/hlfshell/udacity-carnd-traffic_sign_classifier

Last synced: 6 months ago
JSON representation

Using Deep Learning / LeNet CNN architecture to identify traffic signs

• Host: GitHub
• URL: https://github.com/hlfshell/udacity-carnd-traffic_sign_classifier
• Owner: hlfshell
• Created: 2017-12-02T18:01:39.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-12-11T16:09:08.000Z (over 8 years ago)
• Last Synced: 2025-02-09T17:14:47.416Z (over 1 year ago)
• Language: HTML
• Size: 1.89 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Codeowners: CODEOWNERS

Awesome Lists containing this project

README

          
## Project: Build a Traffic Sign Recognition Program

[![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)

Overview

---

In this project, I will be using the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset) combined with the LeNet CNN architecture to create a deep neural network that can identify signs.

This is a project from term 1 of the self driving car nanodegree program at Udacity.

`WRITEUP.md` contains the project writeup.

The `evaluation-signs` folder contains the images used for the "off the internet" testing of the model.

The jupyter notebook should be viewable through the `.pynb` file or the `.html` export of the notebook.