Project: Build a Traffic Sign Recognition Program

The Project

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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

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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.