https://github.com/pierluigiferrari/traffic_sign_classifier

A traffic sign classifier built with TensorFlow
https://github.com/pierluigiferrari/traffic_sign_classifier

recognize-traffic-signs tensorflow traffic-sign-classification traffic-sign-classifier traffic-sign-recognition

Last synced: about 2 months ago
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A traffic sign classifier built with TensorFlow

• Host: GitHub
• URL: https://github.com/pierluigiferrari/traffic_sign_classifier
• Owner: pierluigiferrari
• Created: 2017-02-15T23:46:36.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-11-08T17:09:56.000Z (about 7 years ago)
• Last Synced: 2024-03-20T00:32:51.313Z (10 months ago)
• Topics: recognize-traffic-signs, tensorflow, traffic-sign-classification, traffic-sign-classifier, traffic-sign-recognition
• Language: Python
• Homepage:
• Size: 12.1 MB
• Stars: 15
• Watchers: 2
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Building a Traffic Sign Recognition Classifier

This is a Convolutional Neural Network built with TensorFlow and trained to recognize traffic signs. The dataset used is the [German Traffic Sign Recognition Benchmark](http://benchmark.ini.rub.de/?section=gtsrb) dataset.

### Training and Experimentation Results

The final model achieves an accuracy of 97.9% on the official GTSRB test dataset.

The model includes my own implementation of batch normalization using a running average estimator of the population moments, along with a few tests and visualizations to see what batch normalization does.

All code, training results, and relevant explanations and comments are contained in the iPython notebook in this directory.

### Use Instructions

1. Clone or fork this repository.

2. Launch the Jupyter notebook: `jupyter notebook traffic_sign_classifier.ipynb`

3. Execute the code cells you are interested in. Note that cells may depend on previous cells and/or require the dataset linked below. The notebook explains clearly what each code cell does.

### Dataset

The resized and pickled versions of the official GTSRB training and test datasets that were used for this project can be downloaded [here](https://drive.google.com/open?id=0B0WbA4IemlxlUmlJaDBXbzJHMFE).

### Dependencies

1. Python 3.x

2. TensorFlow 0.1x

4. Numpy

5. OpenCV

6. Matplotlib

Note: TensorFlow 1.0 introduced major syntax changes and this program does not yet support these changes.