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
Pipeline code for Sequential-GAM(Genome Architecture Mapping).

Sequential-GAM Pipeline code for Sequential-GAM(Genome Architecture Mapping). mapping whole_preprocess.sh include the whole processing of mapping. usa

3 Nov 03, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
Custom TensorFlow2 implementations of forward and backward computation of soft-DTW algorithm in batch mode.

Batch Soft-DTW(Dynamic Time Warping) in TensorFlow2 including forward and backward computation Custom TensorFlow2 implementations of forward and backw

19 Aug 30, 2022
[NeurIPS-2021] Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Efficient Graph Similarity Computation - (EGSC) This repo contains the source code and dataset for our paper: Slow Learning and Fast Inference: Effici

24 Dec 31, 2022
Source code of the paper Meta-learning with an Adaptive Task Scheduler.

ATS About Source code of the paper Meta-learning with an Adaptive Task Scheduler. If you find this repository useful in your research, please cite the

Huaxiu Yao 16 Dec 26, 2022
Intelligent Video Analytics toolkit based on different inference backends.

English | 中文 OpenIVA OpenIVA is an end-to-end intelligent video analytics development toolkit based on different inference backends, designed to help

Quantum Liu 15 Oct 27, 2022
Keeper for Ricochet Protocol, implemented with Apache Airflow

Ricochet Keeper This repository contains Apache Airflow DAGs for executing keeper operations for Ricochet Exchange. Usage You will need to run this us

Ricochet Exchange 5 May 24, 2022
JAX bindings to the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) library

JAX bindings to FINUFFT This package provides a JAX interface to (a subset of) the Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT) lib

Dan Foreman-Mackey 32 Oct 15, 2022
Relative Positional Encoding for Transformers with Linear Complexity

Stochastic Positional Encoding (SPE) This is the source code repository for the ICML 2021 paper Relative Positional Encoding for Transformers with Lin

Antoine Liutkus 48 Nov 16, 2022
A mini lib that implements several useful functions binding to PyTorch in C++.

Torch-gather A mini library that implements several useful functions binding to PyTorch in C++. What does gather do? Why do we need it? When dealing w

maxwellzh 8 Sep 07, 2022
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

Attention Gated Networks (Image Classification & Segmentation) Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framewor

Ozan Oktay 1.6k Dec 30, 2022
A real world application of a Recurrent Neural Network on a binary classification of time series data

What is this This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data

Josep Maria Salvia Hornos 2 Jan 30, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
Official Implementation of "Designing an Encoder for StyleGAN Image Manipulation"

Designing an Encoder for StyleGAN Image Manipulation (SIGGRAPH 2021) Recently, there has been a surge of diverse methods for performing image editing

749 Jan 09, 2023
This repo contains implementation of different architectures for emotion recognition in conversations.

Emotion Recognition in Conversations Updates 🔥 🔥 🔥 Date Announcements 03/08/2021 🎆 🎆 We have released a new dataset M2H2: A Multimodal Multiparty

Deep Cognition and Language Research (DeCLaRe) Lab 1k Dec 30, 2022
The fastest way to visualize GradCAM with your Keras models.

VizGradCAM VizGradCam is the fastest way to visualize GradCAM in Keras models. GradCAM helps with providing visual explainability of trained models an

58 Nov 19, 2022
Solving Zero-Shot Learning in Named Entity Recognition with Common Sense Knowledge

Zero-Shot Learning in Named Entity Recognition with Common Sense Knowledge Associated code for the paper Zero-Shot Learning in Named Entity Recognitio

Søren Hougaard Mulvad 13 Dec 25, 2022
FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction

FLAVR is a fast, flow-free frame interpolation method capable of single shot multi-frame prediction. It uses a customized encoder decoder architecture with spatio-temporal convolutions and channel ga

Tarun K 280 Dec 23, 2022
deep-prae

Deep Probabilistic Accelerated Evaluation (Deep-PrAE) Our work presents an efficient rare event simulation methodology for black box autonomy using Im

Safe AI Lab 4 Apr 17, 2021
A geometric deep learning pipeline for predicting protein interface contacts.

A geometric deep learning pipeline for predicting protein interface contacts.

44 Dec 30, 2022