IA for recognising Traffic Signs using Keras [Tensorflow]

Overview

Traffic Signs Recognition ⚠️ 🚦

Fundamentals of Intelligent Systems

Introduction 📄

Development of a neural network capable of recognizing nine different danger warning traffic signs.

  1. Sign P-50. Other dangers.
  2. Signal P-14b. Dangerous curves to the left.
  3. Sign P-20. Pedestrians.
  4. Sign P-19. Slippery pavement.
  5. Sign P-18. Works.
  6. Signal P-3. Traffic lights.
  7. Sign P-15a. Highlight.
  8. Sign P-1. Intersection with priority.
  9. Signal P-24. Passage of animals in freedom.

Screenshot 2022-01-09 at 20 48 42

Datasets 📁

The images, both for the training set and for the validation set, have been extracted from several datasets, the most notable being the GTSRB (German Traffic Sign Recognition Benchmark) dataset: https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb -german-traffic-sign

In addition, self-made images have been added.

Data Augmentation

Data augmentation has been performed on the training set, to obtain new images from those already present. To do this, we have used various parameters such as rotation angle, cut angle, among others, in the ImageDataGenerator class

Additional Information

All the implementation details are in the memory that we have made, which can be read from the following link: https://github.com/nahimaort/Traffic-Signs-Recognition/blob/main/Memoria.pdf In this document, the hyperparameters that have been used are detailed, as well as those that we have tested. Also, the concept of Categorical Cross Entropy is explained, and how this loss function works

Authors ✒️

Base code provided by Cayetano Guerra Artal

Owner
Sebastián Fernández García
I am currently studying a bachelor's degree in computer engineering. My interests range from cryptography, through audiovisual creations to AI
Sebastián Fernández García
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search DropNAS, a grouped operation dropout method for one-level DARTS, with better

weijunhong 4 Aug 15, 2022
TAug :: Time Series Data Augmentation using Deep Generative Models

TAug :: Time Series Data Augmentation using Deep Generative Models Note!!! The package is under development so be careful for using in production! Fea

35 Dec 06, 2022
CVPR 2021

Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-image Translation [Paper] | [Poster] | [Codes] Yahui Liu1,3, Enver Sangineto1,

Yahui Liu 37 Sep 12, 2022
Official page of Patchwork (RA-L'21 w/ IROS'21)

Patchwork Official page of "Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor

Hyungtae Lim 254 Jan 05, 2023
A convolutional recurrent neural network for classifying A/B phases in EEG signals recorded for sleep analysis.

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

Apurva R. Umredkar 2 Nov 25, 2022
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

Yuan Yin 24 Oct 24, 2022
Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

Note: this repo has been discontinued, please check code for newer version of the paper here Weight Normalized GAN Code for the paper "On the Effects

Sitao Xiang 182 Sep 06, 2021
Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.

Fast Training of Neural Lumigraph Representations using Meta Learning Project Page | Paper | Data Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzst

Alex 39 Oct 08, 2022
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

MLCR This is the source code for paper Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition. Xuesong Niu, Hu Han, Shiguang

Edson-Niu 60 Nov 29, 2022
Pytorch implementation of “Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement”

Graph-to-Graph Transformers Self-attention models, such as Transformer, have been hugely successful in a wide range of natural language processing (NL

Idiap Research Institute 40 Aug 14, 2022
Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows.

Swin-Transformer Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows. For more details, ple

旷视天元 MegEngine 9 Mar 14, 2022
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
code for Grapadora research paper experimentation

Road feature embedding selection method Code for research paper experimentation Abstract Traffic forecasting models rely on data that needs to be sens

Eric López Manibardo 0 May 26, 2022
In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021

In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021. Balestriero et

Sean M. Hendryx 1 Jan 27, 2022
The official implementation of ICCV paper "Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds".

Box-Aware Tracker (BAT) Pytorch-Lightning implementation of the Box-Aware Tracker. Box-Aware Feature Enhancement for Single Object Tracking on Point C

Kangel Zenn 5 Mar 26, 2022
Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

Natural Language Processing @UCLA 463 Dec 09, 2022
Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022.

Jadena Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022. arXiv

Qing Guo 13 Nov 29, 2022
This code implements constituency parse tree aggregation

README This code implements constituency parse tree aggregation. Folder details code: This folder contains the code that implements constituency parse

Adithya Kulkarni 0 Oct 11, 2021
Distance correlation and related E-statistics in Python

dcor dcor: distance correlation and related E-statistics in Python. E-statistics are functions of distances between statistical observations in metric

Carlos Ramos Carreño 108 Dec 27, 2022
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The

Benedek Rozemberczki 188 Dec 29, 2022