Data
GTSRB-HOG02

GTSRB-HOG02

active ARFF The data is free to use (when citing the publication). Visibility: public Uploaded 24-07-2019 by Matthias Feurer
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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 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. \# HOG features The 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. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. IEEE Conference on Computer Vision and Pattern Recognition, pages 886-893, 2005

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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
pixel-00138numeric48288 unique values
0 missing
pixel-00394numeric49114 unique values
0 missing
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0 missing
pixel-00906numeric50002 unique values
0 missing
pixel-01162numeric49075 unique values
0 missing
pixel-01418numeric48817 unique values
0 missing
pixel-00139numeric48350 unique values
0 missing
pixel-00395numeric48834 unique values
0 missing
pixel-00651numeric48565 unique values
0 missing
pixel-00907numeric49440 unique values
0 missing
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0 missing
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0 missing
pixel-00140numeric48059 unique values
0 missing
pixel-00396numeric48539 unique values
0 missing
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0 missing
pixel-00908numeric46447 unique values
0 missing
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0 missing
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0 missing
pixel-00141numeric47021 unique values
0 missing
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0 missing
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0 missing
pixel-00909numeric48448 unique values
0 missing
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0 missing
pixel-01421numeric40301 unique values
0 missing
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0 missing
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0 missing
pixel-00654numeric49675 unique values
0 missing
pixel-00910numeric48796 unique values
0 missing
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0 missing
pixel-01422numeric48764 unique values
0 missing
pixel-00143numeric49183 unique values
0 missing
pixel-00399numeric49830 unique values
0 missing
pixel-00655numeric48187 unique values
0 missing
pixel-00911numeric48059 unique values
0 missing
pixel-01167numeric49357 unique values
0 missing
pixel-01423numeric49626 unique values
0 missing
pixel-00144numeric49258 unique values
0 missing
pixel-00400numeric48787 unique values
0 missing
pixel-00656numeric44091 unique values
0 missing
pixel-00912numeric47400 unique values
0 missing
pixel-01168numeric47748 unique values
0 missing
pixel-01424numeric48134 unique values
0 missing
pixel-00145numeric44605 unique values
0 missing
pixel-00401numeric47350 unique values
0 missing
pixel-00657numeric49942 unique values
0 missing
pixel-00913numeric48907 unique values
0 missing
pixel-01169numeric48983 unique values
0 missing
pixel-01425numeric47855 unique values
0 missing
pixel-00146numeric48807 unique values
0 missing
pixel-00402numeric49822 unique values
0 missing
pixel-00658numeric50150 unique values
0 missing
pixel-00914numeric49052 unique values
0 missing
pixel-01170numeric49464 unique values
0 missing
pixel-01426numeric49012 unique values
0 missing
pixel-00147numeric49267 unique values
0 missing
pixel-00403numeric50065 unique values
0 missing
pixel-00659numeric48546 unique values
0 missing
pixel-00915numeric48706 unique values
0 missing
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0 missing
pixel-01427numeric49217 unique values
0 missing
pixel-00148numeric48590 unique values
0 missing
pixel-00404numeric48926 unique values
0 missing
pixel-00660numeric43943 unique values
0 missing
pixel-00916numeric47714 unique values
0 missing
pixel-01172numeric47714 unique values
0 missing
pixel-01428numeric48803 unique values
0 missing
pixel-00149numeric43015 unique values
0 missing
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0 missing
pixel-00661numeric49735 unique values
0 missing
pixel-00917numeric46804 unique values
0 missing
pixel-01173numeric47015 unique values
0 missing
pixel-01429numeric45953 unique values
0 missing
pixel-00150numeric47417 unique values
0 missing
pixel-00406numeric47130 unique values
0 missing
pixel-00662numeric49540 unique values
0 missing
pixel-00918numeric46720 unique values
0 missing
pixel-01174numeric47891 unique values
0 missing
pixel-01430numeric46903 unique values
0 missing
pixel-00151numeric49334 unique values
0 missing
pixel-00407numeric49077 unique values
0 missing
pixel-00663numeric47847 unique values
0 missing
pixel-00919numeric47097 unique values
0 missing
pixel-01175numeric48032 unique values
0 missing
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0 missing
pixel-00152numeric49651 unique values
0 missing
pixel-00408numeric46744 unique values
0 missing
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0 missing
pixel-00920numeric46533 unique values
0 missing
pixel-01176numeric47902 unique values
0 missing
pixel-01432numeric47725 unique values
0 missing
pixel-00153numeric46759 unique values
0 missing
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0 missing
pixel-00665numeric48980 unique values
0 missing
pixel-00921numeric49469 unique values
0 missing
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0 missing
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0 missing
pixel-00154numeric48560 unique values
0 missing
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0 missing
pixel-00666numeric49294 unique values
0 missing
pixel-00922numeric49818 unique values
0 missing
pixel-01178numeric48708 unique values
0 missing
pixel-01434numeric48683 unique values
0 missing
pixel-00155numeric49463 unique values
0 missing
pixel-00411numeric49378 unique values
0 missing
pixel-00667numeric48193 unique values
0 missing
pixel-00923numeric48966 unique values
0 missing
pixel-01179numeric49341 unique values
0 missing
pixel-01435numeric48824 unique values
0 missing
pixel-00156numeric49224 unique values
0 missing
pixel-00412numeric47027 unique values
0 missing
pixel-00668numeric47396 unique values
0 missing
pixel-00924numeric46593 unique values
0 missing
pixel-01180numeric47551 unique values
0 missing
pixel-01436numeric48552 unique values
0 missing
pixel-00157numeric45178 unique values
0 missing
pixel-00413numeric48257 unique values
0 missing
pixel-00669numeric49667 unique values
0 missing
pixel-00925numeric48130 unique values
0 missing
pixel-01181numeric44934 unique values
0 missing
pixel-01437numeric44281 unique values
0 missing
pixel-00158numeric48036 unique values
0 missing
pixel-00414numeric49585 unique values
0 missing
pixel-00670numeric49579 unique values
0 missing
pixel-00926numeric48427 unique values
0 missing
pixel-01182numeric48832 unique values
0 missing
pixel-01438numeric46764 unique values
0 missing
pixel-00159numeric49590 unique values
0 missing
pixel-00415numeric48909 unique values
0 missing
pixel-00671numeric48404 unique values
0 missing
pixel-00927numeric47381 unique values
0 missing
pixel-01183numeric49024 unique values
0 missing
pixel-01439numeric47783 unique values
0 missing
pixel-00160numeric49335 unique values
0 missing
pixel-00416numeric49154 unique values
0 missing
pixel-00672numeric49144 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing

62 properties

51839
Number of instances (rows) of the dataset.
1569
Number of attributes (columns) of the dataset.
43
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
1568
Number of numeric attributes.
1
Number of nominal attributes.
4.65
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
2.1
Third quartile of skewness among attributes of the numeric type.
0.24
Maximum standard deviation of attributes of the numeric type.
0.52
Percentage of instances belonging to the least frequent class.
0.72
First quartile of kurtosis among attributes of the numeric type.
0.13
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
270
Number of instances belonging to the least frequent class.
0.07
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
3.67
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Mean of means among attributes of the numeric type.
1.11
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.09
First quartile of standard deviation of attributes of the numeric type.
0.77
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
5.02
Entropy of the target attribute values.
43
Average number of distinct values among the attributes of the nominal type.
2.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
1.64
Mean skewness among attributes of the numeric type.
0.1
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
5.79
Percentage of instances belonging to the most frequent class.
0.11
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3000
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.58
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.54
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.11
Second quartile (Median) of standard deviation of attributes of the numeric type.
29.25
Maximum kurtosis among attributes of the numeric type.
0.02
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.35
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
5.1
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
43
The minimal number of distinct values among attributes of the nominal type.
99.94
Percentage of numeric attributes.
0.14
Third quartile of means among attributes of the numeric type.
43
The maximum number of distinct values among attributes of the nominal type.
-0.56
Minimum skewness among attributes of the numeric type.
0.06
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.

13 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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