Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Program

Overview

This project uses deep neural networks and convolutional neural networks to classify traffic signs. The model is trained to decode traffic signs from natural images using the German Traffic Sign Dataset.

The classifier is implemented in Tensorflow.

Dependencies

This project requires Python 3.5 and the following Python libraries installed:

• Jupyter
• NumPy
• SciPy
• scikit-learn
• TensorFlow
• Matplotlib
• Pandas (Optional)

Run this command at the terminal prompt to install OpenCV. Useful for image processing:

• conda install -c https://conda.anaconda.org/menpo opencv3

Dataset

1. Download the dataset. This is a pickled dataset in which we've already resized the images to 32x32.
2. Clone the project and start the notebook.

git clone https://github.com/udacity/CarND-Traffic-Signs
cd CarND-Traffic-Signs
jupyter notebook Traffic_Signs_Recognition.ipynb

3. Follow the instructions in the Traffic_Signs_Recognition.ipynb notebook.