gtsrb

README.md

@@ -18,7 +18,7 @@ task_ids:
   - multi-label-image-classification
 ---
 
-# Dataset Card for [Needs More Information]
+# Dataset Card for GTSRB
 
 ## Table of Contents
 
@@ -50,37 +50,38 @@ task_ids:
 - **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]
+- **Point of Contact:** [email protected]
 
 ### 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.
+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:
 
-### Supported Tasks and Leaderboards
-
-[Needs More Information]
+- Single-image, multi-class classification problem
+- More than 40 classes
+- More than 50,000 images in total
+- Large, lifelike database
 
-### Languages
+### Supported Tasks and Leaderboards
 
-en
+[Kaggle](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) \
+[Original](https://benchmark.ini.rub.de/gtsrb_results.html)
 
 ## 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,
+  "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