Classify traffic signs by three classic ConvNets architecture using GTSRB dataset.

A traffic sign classifier built with TensorFlow

A traffic sign classifier using LeNet for Self driving cars

This program uses a deep neural network with several convolutional layers to classify traffic signs. The model is able to recognize traffic signs with an accuracy of 96,2%. It was trained and validated using the German Traffic Sign Dataset with 43 classes (types of traffic signs) and more than 50,000 images in total.

This is a Classifier Algorithm that can classify German Traffic-Signs. It uses the good old convolution network inspired by the Nvidia Model used in their self-driving car.

This Python script generates a synthetic dataset of traffic sign images in COCO format, intended for training and testing object detection models. The dataset includes various traffic sign overlays placed on diverse background images, offering a wide range of scenarios to enhance model robustness.

Traffic Sign Classifier using a convolutional neural network

Traffic sign classifier using OpenCV, LeNet-5, AlexNet

CNN and Data Augmentation to train a traffic sign classifier with OpenCv and Tensorflow

My solution to the Udacity Self-Driving Car Engineer Nanodegree Traffic Sign Classifier project.

Traffic Sign Classification with Convolutional Neural Networks in Python

Udacity: Self-Driving Car Engineer Nanodegree | Project: Traffic Sign Classification

Created a traffic sign classifier with a CNN.

Traffic-Sign-Classifier

Convolutional Neural Network using Jupyter Notebooks

🏎️ Traffic Sign Classifier Project using NN with TensorFlow for the Self-Driving Car Nanodegree at Udacity

Convolutional Neural Net to Classify Traffic Signs

Traffic Sign Classifier Project based on TensorFlow in Udacity Self Driving Course Term 1.

Classify the Trafic Signs using deep learning model with Tensorflow

Self Driving Car NanoDegree - Project 2 - Traffic Sign Classifier