In this project, you will use what you've learned about deep neural networks and convolutional neural networks to classify traffic signs. You will train a model so it can decode traffic signs from natural images by using the German Traffic Sign Dataset. After the model is trained, you will then test your model program on new images of traffic signs you find on the web, or, if you're feeling adventurous pictures of traffic signs you find locally!
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
Download the data set from https://d17h27t6h515a5.cloudfront.net/topher/2016/November/581faac4_traffic-signs-data/traffic-signs-data.zip
For a good introduction to CNN: https://brohrer.github.io/how_convolutional_neural_networks_work.html