Self-Driving Nano Degree Program : Traffic Sign Recognition
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Updated
Apr 21, 2017 - Jupyter Notebook
Self-Driving Nano Degree Program : Traffic Sign Recognition
Build a Traffic Sign Recognition Program
Classification of German traffic signs using deep neural network. Lenet is used as model to train on data set provided by Udacity. The result comprises prediction on downloaded traffic sign images.
Convolutional Neural Net to Classify Traffic Signs
Solving German Traffic Signs Classification problem with VGG model
building a classifier to recognize traffic signs using deep neural networks and convolutional neural networks.
Traffic Sign classifier based on TensorFLow
Traffic sign recognition project of Udacity Self-driving car nano degree
German-Traffic-Sign-Classification
I utilized deep neural networks and convolutional neural networks to classify traffic signs. I trained and validated a model so it can classify traffic sign images using the German Traffic Sign Dataset.
Third Project in the Udacity Self-Driving Car Nanodegree where Deep Neural Networks and Convolutional Neural Networks are used to classify traffic signs.
From traffic sign database downloaded from https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign It is produced using a CNN based on resnet and pytorch, a model to recognize traffic signs previously detected using the model produced at https://github.com/ablanco1950/DetectTrafficSign
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