This project is about classifying traffic signs using deep neural networks and convolutional neural networks. Using the German Traffic Sign Dataset and later trying to classify new images. Trained on an AWS GPU instance (g2.2large).
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
This lab requires:
The lab enviroment can be created with CarND Term1 Starter Kit. Click here for the details.
- 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.
- 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.ipynbThis proyect is Copyright © 2016-2017 Lucas Gago. It is free software, and may be redistributed under the terms specified in the MIT Licence.