This project is part of Udacity's Self-driving Car Engineer Nanodegree program. The goal of this proejct is build a Convolutional Neural Network (CNN) that recognizes traffic signs.
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
- Summarize the results with a written report
Project output:
- All the code for this project is in the Traffic_Sign_Classifier.ipynb notebook.
- The file Traffic_Sign_Classifier.html contains a HTML export of the notebook for online viewing.
- The project report is in writeup.md.
My classifier has a validation accuracy of 97.43%, and correctly classifies 100% of the previously-unseen test images.
The notebook provides more detail on the design of the CNN classifier, and the results.
The Traffic_Sign_Classifier.ipynb notebook contains all the code for this project.
I used the following primary libraries:
- TensorFlow (version 2.5.0)
- Keras (version 2.4.3)
- OpenCV (version 4.5.1)
- numpy
- pandas
- matplotlib
The requirements.txt contains a full listing of the dependencies I used, and can be used to create a local virtual environment in which the notebook will run.
