In this project, I used a convolutional neural networks to classify traffic signs. I have trained a model inspired by VGG-Net so it can decode traffic signs from natural images by using the German Traffic Sign Dataset.
After the model is trained, user can test 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 dataset. This is a pickled dataset in which we've already resized the images to 32x32.
- 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
- Follow the instructions in the
Traffic_Signs_Recognition.ipynbnotebook.