https://github.com/piyp791/udacity-sdcndp-traffic-sign-classification

Traffic Sign Classification using Deep Learning, done as a part of Udacity Self Driving Car Nanodegree Program
https://github.com/piyp791/udacity-sdcndp-traffic-sign-classification

classify-traffic-signs deep-learning ipython-notebook python traffic-sign-classification udacity-self-driving-car

Last synced: 6 months ago
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Traffic Sign Classification using Deep Learning, done as a part of Udacity Self Driving Car Nanodegree Program

• Host: GitHub
• URL: https://github.com/piyp791/udacity-sdcndp-traffic-sign-classification
• Owner: piyp791
• Created: 2017-07-01T09:59:59.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2024-12-05T12:17:33.000Z (11 months ago)
• Last Synced: 2025-03-28T02:53:25.614Z (7 months ago)
• Topics: classify-traffic-signs, deep-learning, ipython-notebook, python, traffic-sign-classification, udacity-self-driving-car
• Language: Jupyter Notebook
• Homepage: https://bitsandmusic.com/post/traffic-sign-classification/
• Size: 11.5 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Project: Build a Traffic Sign Recognition Program

[![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)

### The Project

---

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

### Project Approach and Results

---

https://bitsandmusic.com/post/traffic-sign-classification/

### Project Set Up

1. Download the data set from [here](https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip). This is a pickled dataset in which we've already resized the images to 32x32. It contains a training, validation and test set.

2. Set up the [CarND Term1 Starter Kit](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/83ec35ee-1e02-48a5-bdb7-d244bd47c2dc/lessons/8c82408b-a217-4d09-b81d-1bda4c6380ef/concepts/4f1870e0-3849-43e4-b670-12e6f2d4b7a7).

2. Open the code in a Jupyter Notebook

To start Jupyter in your browser, use terminal to navigate to your project directory and then run the following command at the terminal prompt (be sure you've activated your Python 3 carnd-term1 environment as described in the [CarND Term1 Starter Kit](https://github.com/udacity/CarND-Term1-Starter-Kit/blob/master/README.md) installation instructions!):

`> jupyter notebook`

A browser window will appear showing the contents of the current directory.  Click on the file called "Traffic_Sign_Classifier.ipynb".  Another browser window will appear displaying the notebook.  Follow the instructions in the notebook to run the project.  

```