rdjondo/CarND-Traffic-Sign-Classifier-Project

Classify Traffic Signs.

Project: Build a Traffic Sign Recognition Program

Project forked from

Overview

In this project, I have used what I've learned about deep neural networks and convolutional neural networks to classify traffic signs. I trained a model so it can decode traffic signs from natural images by using the German Traffic Sign Dataset.

My results are documented in this python notebook Traffic_Signs_Recognition.ipynb.

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.