bazyl/GTSRB

Dataset Card for GTSRB

Table of Contents

• —Dataset Description
• —Dataset Summary
• —Supported Tasks
• —Languages
• —Dataset Structure
• —Data Instances
• —Data Fields
• —Data Splits
• —Dataset Creation
• —Curation Rationale
• —Source Data
• —Annotations
• —Personal and Sensitive Information
• —Considerations for Using the Data
• —Social Impact of Dataset
• —Discussion of Biases
• —Other Known Limitations
• —Additional Information
• —Dataset Curators
• —Licensing Information
• —Citation Information

Dataset Description

• —Homepage: http://www.sciencedirect.com/science/article/pii/S0893608012000457
• —Repository: https://github.com/bazylhorsey/gtsrb/
• —Paper: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
• —Leaderboard: https://benchmark.ini.rub.de/gtsrb_results.html
• —Point of Contact: bhorsey16@gmail.com

Dataset Summary

The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:

• —Single-image, multi-class classification problem
• —More than 40 classes
• —More than 50,000 images in total
• —Large, lifelike database

Supported Tasks and Leaderboards

Kaggle \ Original

Dataset Structure

Data Instances

{
  "Width": 31,
  "Height": 31,
  "Roi.X1": 6,
  "Roi.Y1": 6,
  "Roi.X2": 26,
  "Roi.Y2": 26,
  "ClassId": 20,
  "Path": "Train/20/00020_00004_00002.png",
}

Data Fields

• —Width: width of image
• —Height: Height of image
• —Roi.X1: Upper left X coordinate
• —Roi.Y1: Upper left Y coordinate
• —Roi.X2: Lower right t X coordinate
• —Roi.Y2: Lower right Y coordinate
• —ClassId: Class of image
• —Path: Path of image

Data Splits

Categories: 42 Train: 39209 Test: 12630

Dataset Creation

Curation Rationale

Recognition of traffic signs is a challenging real-world problem of high industrial relevance. Although commercial systems have reached the market and several studies on this topic have been published, systematic unbiased comparisons of different approaches are missing and comprehensive benchmark datasets are not freely available.

Traffic sign recognition is a multi-class classification problem with unbalanced class frequencies. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, there exist subsets of classes (e. g., speed limit signs) that are very similar to each other.

The classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc.

Humans are capable of recognizing the large variety of existing road signs with close to 100% correctness. This does not only apply to real-world driving, which provides both context and multiple views of a single traffic sign, but also to the recognition from single images.

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Initial Data Collection and Normalization

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Annotations

Annotation process

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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