readme

README.md

@@ -1,3 +1,163 @@
----
-license: gnu general public
----
+annotations_creators:
+
+- crowdsourced
+  language_creators:
+- found
+  languages: []
+  licenses:
+- gpl-3.0-or-later
+  multilinguality: []
+  pretty_name: GTSRB
+  size_categories:
+- 10K<n<100K
+  source_datasets:
+- original
+  task_categories:
+- image-classification
+  task_ids:
+- multi-label-image-classification
+
+# Dataset Card for [Needs More Information]
+
+## Table of Contents
+
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation 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:** [Needs More Information]
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+en
+
+## 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.
+
+<!-- ### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+[Needs More Information] -->

gtsrb.py

@@ -64,7 +64,7 @@ _CITATION = """\
 _DESCRIPTION = """\
 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 classifer has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc. \
+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.
 """