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 and validate a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model is trained, you will then try out your model on images of German traffic signs that you find on the web.
We have included an Ipython notebook that contains further instructions and starter code. Be sure to download the Ipython notebook.
We also want you to create a detailed writeup of the project. Check out the writeup template for this project and use it as a starting point for creating your own writeup. The writeup can be either a markdown file or a pdf document.
To meet specifications, the project will require submitting three files:
- the Ipython notebook with the code
- the code exported as an html file
- a writeup report either as a markdown or pdf file
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
| File / Directory | Description |
|---|---|
| report | Directory with image files for writeup_report.md |
| images_f_web | Image files exacted from Web for Traffic_Sign_Classifier.ipynb |
| traffic-signs-data | Training, Test and validation data |
| Traffic_Sign_Classifier.ipynb | Jupyter notebook file |
| Traffic_Sign_Classifier.html | Export from Jupyter notebook including all output |
| writeup_report.md | Writeup report for this project |
| signnames.csv | Label list of traffic sign |
Note: Traffic_Sign_Classifier.ipynb was executed with python 2.7.
This lab requires:
The lab environment can be created with CarND Term1 Starter Kit. Click here for the details.
##Repository from Udacity
- Download the data set. The classroom has a link to the data set in the "Project Instructions" content. This is a pickled dataset in which we've already resized the images to 32x32. It contains a training, validation and test set.
- Clone the project, which contains the Ipython notebook and the writeup template.
git clone https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project
cd CarND-Traffic-Sign-Classifier-Project
jupyter notebook Traffic_Sign_Classifier.ipynb##Repository with updated files Updated files were saved into here.
Follow the instructions in the Traffic_Sign_Classifier.ipynb notebook and write the project report using the writeup template as a guide, writeup_template.md. Submit the project code and writeup document.
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