https://github.com/awav/traffic-sign-resnet
Traffic sign classification using Resnet deep network
https://github.com/awav/traffic-sign-resnet
deep-learning machine-learning resnet traffic-sign-classification
Last synced: 7 months ago
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Traffic sign classification using Resnet deep network
- Host: GitHub
- URL: https://github.com/awav/traffic-sign-resnet
- Owner: awav
- Created: 2016-12-23T23:34:54.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-02-05T14:33:42.000Z (over 8 years ago)
- Last Synced: 2025-01-30T03:15:32.954Z (9 months ago)
- Topics: deep-learning, machine-learning, resnet, traffic-sign-classification
- Language: HTML
- Homepage:
- Size: 62.2 MB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Self-Driving Car Engineer Nanodegree
# Deep Learning
## Project: Build a Traffic Sign Recognition Program
### Overview
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](http://benchmark.ini.rub.de/?section=gtsrb&subsection=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!
### Dependencies
This project requires **Python 3.5** and the following Python libraries installed:
- [Jupyter](http://jupyter.org/)
- [NumPy](http://www.numpy.org/)
- [SciPy](https://www.scipy.org/)
- [scikit-learn](http://scikit-learn.org/)
- [TensorFlow](http://tensorflow.org)
- [Matplotlib](http://matplotlib.org/)
- [Pandas](http://pandas.pydata.org/) (Optional)
Run this command at the terminal prompt to install [OpenCV](http://opencv.org/). Useful for image processing:
- `conda install -c https://conda.anaconda.org/menpo opencv3`
### Dataset
1. [Download the dataset](https://d17h27t6h515a5.cloudfront.net/topher/2016/November/581faac4_traffic-signs-data/traffic-signs-data.zip). 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.