GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → upul → Traffic-Signs

upul / Traffic-Signs

Licence: other
Second Project of the Udacity Self-Driving Car Nanodegree Program

Programming Languages

HTML
75241 projects
Jupyter Notebook
11667 projects
python
139335 projects - #7 most used programming language

Labels

deep-learningtensorflowclassificationself-driving-car

Projects that are alternatives of or similar to Traffic-Signs

Grid Gcn
Grid-GCN for Fast and Scalable Point Cloud Learning
Stars: ✭ 143 (+308.57%)
Mutual labels:  classification, self-driving-car
Selfdrivingcar
A collection of all projects pertaining to different layers in the SDC software stack
Stars: ✭ 107 (+205.71%)
Mutual labels:  classification, self-driving-car
classification
Catalyst.Classification
Stars: ✭ 35 (+0%)
Mutual labels:  classification
SecondaryAwesomeCollection
收集深度学习相关的awesome 资源列表,欢迎补充
Stars: ✭ 75 (+114.29%)
Mutual labels:  self-driving-car
10 days of deep learning
10 days 10 different practical applications of Deep Learning (primarily NLP) using Tensorflow and Keras
Stars: ✭ 28 (-20%)
Mutual labels:  classification
YOLOv1 tensorflow
YOLOv1 tensorflow
Stars: ✭ 14 (-60%)
Mutual labels:  classification
NIPS-Global-Paper-Implementation-Challenge
Selective Classification For Deep Neural Networks.
Stars: ✭ 11 (-68.57%)
Mutual labels:  classification
Hybrid-A-Star-U-Turn-Solution
Autonomous driving trajectory planning solution for U-Turn scenario
Stars: ✭ 75 (+114.29%)
Mutual labels:  self-driving-car
omikuji
An efficient implementation of Partitioned Label Trees & its variations for extreme multi-label classification
Stars: ✭ 69 (+97.14%)
Mutual labels:  classification
supervised-machine-learning
This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…
Stars: ✭ 34 (-2.86%)
Mutual labels:  classification
egfr-att
Drug effect prediction using neural network
Stars: ✭ 17 (-51.43%)
Mutual labels:  classification
mmrazor
OpenMMLab Model Compression Toolbox and Benchmark.
Stars: ✭ 644 (+1740%)
Mutual labels:  classification
immuneML
immuneML is a platform for machine learning analysis of adaptive immune receptor repertoire data.
Stars: ✭ 41 (+17.14%)
Mutual labels:  classification
Self-Driving-Car-Steering-Simulator
The aim of this project is to allow a self driving car to steer autonomously in a virtual environment.
Stars: ✭ 15 (-57.14%)
Mutual labels:  self-driving-car
Fraud-Detection-in-Online-Transactions
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
Stars: ✭ 41 (+17.14%)
Mutual labels:  classification
PSCN
A python implementation of Patchy-San Convolutional Network for Graph
Stars: ✭ 39 (+11.43%)
Mutual labels:  classification
Point2Sequence
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
Stars: ✭ 34 (-2.86%)
Mutual labels:  classification
MachineLearning
Machine learning code base of Meng Li
Stars: ✭ 22 (-37.14%)
Mutual labels:  self-driving-car
Monocular-Vehicle-Localization
Estimating the orientation and the relative dimensions of vehicles by producing a 3d bounding frame
Stars: ✭ 28 (-20%)
Mutual labels:  self-driving-car
Machine Learning From Scratch
Machine Learning models from scratch with a better visualisation
Stars: ✭ 15 (-57.14%)
Mutual labels:  classification
View All Similar Projects ➔

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. 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:

Run this command at the terminal prompt to install OpenCV. Useful for image processing:

  • conda install -c https://conda.anaconda.org/menpo opencv3

Dataset

  1. Download the dataset. 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
  1. Follow the instructions in the Traffic_Signs_Recognition.ipynb notebook.
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].