Reproduce GTSRB results of classic deep learning papers.
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Updated
Nov 14, 2020 - Jupyter Notebook
Reproduce GTSRB results of classic deep learning papers.
Traffic sign detection and classification
Code for the paper entitled "Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods".
In this project, a traffic sign recognition system, divided into two parts, is presented. The first part is based on classical image processing techniques, for traffic signs extraction out of a video, whereas the second part is based on machine learning, more explicitly, convolutional neural networks, for image labeling.
A traffic sign classifier using LeNet for Self driving cars
Detection and recognition of traffic signs.
GTSRB - German Traffic Sign Recognition
Introduction to CNNs in TensorFlow
We build a traffic sign classifier with multi-scale Convolutional Networks using Keras
Fast and accurate ResNet for the GTSRB dataset
Tensorflow2, working on Mnist and GTSRB
Training a VGG16 Network to Classify Traffic Signs using the German Traffic Sign Recognition Benchmark (GTSRB)
Classify traffic signs by using the AlexNet and GoogLeNet architecture using GTSRB dataset and comparing the two
Detect traffic sign and recognize them using Image Processing algorithms and Machine Learning(Random Forest)
AI for Autonomous Vehicles
Use of Deep neural networks and convolutional neural networks to classify German traffic signs. Try this app :- https://trafficsignapp.herokuapp.com/
🚸⛔Novel Deep Convolutional Network is proposed for traffic sign classification that achieves outstanding performance on GTSRB surpassing the best human performance of 98.84%.
Speed Limit Recognition using VGG19 architecture on GTSRB dataset in Caffe
Traffic sign classifier with tensorflow
Experimental Adversarial Attack notebooks on CV models
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