{ "data_id": "41988", "name": "GTSRB-HOG02", "exact_name": "GTSRB-HOG02", "version": 1, "version_label": null, "description": "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.\n\n# Pre-calculated features\n\nTo allow scientists without a background in image processing to participate, we several provide pre-calculated feature sets. Each feature set contains the same directory structure as the training image set. For details on the parameters of the feature algorithm, please have a look at the file Feature_description.txt which is part of each archive file.\n \n\\# HOG features\n \nThe file contains three sets of differently configured HOG features (Histograms of Oriented Gradients). The sets contain feature vectors of length 1568, 1568, and 2916 respectively. The features were calculated using the source code from http:\/\/pascal.inrialpes.fr\/soft\/olt\/. For detailed information on HOG, we refer to\n\nN. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. IEEE Conference on Computer Vision and Pattern Recognition, pages 886-893, 2005", "format": "arff", "uploader": "Matthias Feurer", "uploader_id": 86, "visibility": "public", "creator": "\"Johannes Stallkamp, Marc Schlipsing, Jan Salmen, Christian Igel\"", "contributor": "\"Johannes Stallkamp, Marc Schlipsing, Jan Salmen, Christian Igel\"", "date": "2019-07-24 00:13:19", "update_comment": null, "last_update": "2019-07-24 00:13:19", "licence": "The data is free to use (when citing the publication).", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/21388977\/dataset", "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "GTSRB-HOG02", "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. # Pre-calculated features To allow scientists without a background in image processing to participate, we several provide pre-calculated feature sets. Each feature set contains " ], "weight": 5 }, "qualities": { "NumberOfInstances": 51839, "NumberOfFeatures": 1569, "NumberOfClasses": 43, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 1568, "NumberOfSymbolicFeatures": 1, "MeanNominalAttDistinctValues": 43, "Quartile2KurtosisOfNumericAtts": 2.442183145854837, "Dimensionality": 0.030266787553772257, "MeanSkewnessOfNumericAtts": 1.6442582381820543, "Quartile2MeansOfNumericAtts": 0.09868805183265422, "EquivalentNumberOfAtts": null, "MeanStdDevOfNumericAtts": 0.11082774521682298, "Quartile2MutualInformation": null, "MajorityClassPercentage": 5.78714867184938, "MinAttributeEntropy": null, "Quartile2SkewnessOfNumericAtts": 1.5761253467120664, "MajorityClassSize": 3000, "MinKurtosisOfNumericAtts": -1.5373354195923428, "PercentageOfBinaryFeatures": 0, "Quartile2StdDevOfNumericAtts": 0.10726561225217295, "MaxAttributeEntropy": null, "MinMeansOfNumericAtts": 0.020667216138166643, "PercentageOfInstancesWithMissingValues": 0, "Quartile3AttributeEntropy": null, "MaxKurtosisOfNumericAtts": 29.246526125523665, "MinMutualInformation": null, "PercentageOfMissingValues": 0, "Quartile3KurtosisOfNumericAtts": 5.1000055144794345, "MaxMeansOfNumericAtts": 0.3516726891476221, "MinNominalAttDistinctValues": 43, "PercentageOfNumericFeatures": 99.93626513702995, "Quartile3MeansOfNumericAtts": 0.13769269785241373, "MaxMutualInformation": null, "MinSkewnessOfNumericAtts": -0.5646763443273989, "PercentageOfSymbolicFeatures": 0.06373486297004462, "Quartile3MutualInformation": null, "MaxNominalAttDistinctValues": 43, "MinStdDevOfNumericAtts": 0.037559901237545296, "Quartile1AttributeEntropy": null, "Quartile3SkewnessOfNumericAtts": 2.101706756349244, "MaxSkewnessOfNumericAtts": 4.648217839838365, "MinorityClassPercentage": 0.5208433804664442, "Quartile1KurtosisOfNumericAtts": 0.7162655486897211, "Quartile3StdDevOfNumericAtts": 0.12987326303030144, "MaxStdDevOfNumericAtts": 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