https://github.com/charlescatta/german-traffic-sign-classifier

A German traffic sign classifier using a CNN architecture
https://github.com/charlescatta/german-traffic-sign-classifier

Last synced: 2 days ago
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A German traffic sign classifier using a CNN architecture

• Host: GitHub
• URL: https://github.com/charlescatta/german-traffic-sign-classifier
• Owner: charlescatta
• Created: 2017-07-06T12:14:12.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-08-03T12:36:57.000Z (over 7 years ago)
• Last Synced: 2023-12-21T18:35:42.491Z (11 months ago)
• Language: Jupyter Notebook
• Homepage: https://htmlpreview.github.io/?https://github.com/Charles-Catta/German-Traffic-Sign-Classifier/blob/master/writeup.html
• Size: 31.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Project: Traffic Sign Classifier CNN

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

Overview

---

A convolutional neural network that classifies traffic signs with 95% accuracy.

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

### Running to code

The easiest way to run the Jupyter notebook is by using [Docker](https://store.docker.com/search?type=edition&offering=community)

Run the following to get started, this will pull a Docker container with all the required dependencies

```sh

git clone https://github.com/Charles-Catta/German-Traffic-Sign-Classifier.git

cd German-Traffic-Sign-Classifier

```

#### CPU only

```sh

docker run -it --rm -p 8888:8888 -v `pwd`:/src udacity/carnd-term1-starter-kit

```

#### With GPU support

Make sure that [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) is installed

```sh

nvidia-docker run -it --rm -p 8888:8888 -v `pwd`:/src udacity/carnd-term1-starter-kit

```

Go to [localhost:8888](http://localhost:8888/) and insert the token printed to the console

---

### Quick links

[Code](https://nbviewer.jupyter.org/github/Charles-Catta/German-Traffic-Sign-Classifier/blob/master/Traffic_Sign_Classifier.ipynb) (This is a jupyter notebook)

[Write up](https://htmlpreview.github.io/?https://github.com/Charles-Catta/German-Traffic-Sign-Classifier/blob/master/writeup.html)

---

## Example outputs

![softmax_prediction](https://github.com/Charles-Catta/German-Traffic-Sign-Classifier/raw/master/img/softmax_preds_0.png)

![softmax_prediction](https://github.com/Charles-Catta/German-Traffic-Sign-Classifier/raw/master/img/softmax_preds_1.png)