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## Meta-Album Textures-DTD Dataset (Micr) * The Textures DTD dataset(https://www.rbts.x.ac.uk/~vgg/data/dtd/index.html) is a large textures dataset which cnsists f 5 640 images. The data is cllected frm Ggle and Flicker by the riginal authrs f the paper "Describing Textures in the Wild". The data was anntated using Amazn Mechanical Turk. The data cllectin prcess is mentined n the dataset verview page. Fr Meta-Album meta-dataset, this dataset is preprcessed by crpping the images t square images and then resizing them t 128x128 using Open-CV with an anti-aliasing filter. This dataset has 47 class labels. ### Dataset Details ![](https://meta-album.github.i/assets/img/samples/TEX_DTD.png) Meta Album ID: MNF.TEX_DTD Meta Album URL: [https://meta-album.github.i/datasets/TEX_DTD.html](https://meta-album.github.i/datasets/TEX_DTD.html) Dmain ID: MNF Dmain Name: Manufacturing Dataset ID: TEX_DTD Dataset Name: Textures-DTD Shrt Descriptin: Textures dataset frm Describable Textures Dataset \# Classes: 20 \# Images: 800 Keywrds: textures, manufacturing Data Frmat: images Image size: 128x128 License (riginal data release): Open fr research purpses License (Meta-Album data release): CC BY-NC 4.0 License URL (Meta-Album data release): [https://creativecmmns.rg/licenses/by-nc/4.0/](https://creativecmmns.rg/licenses/by-nc/4.0/) Surce: Describable Textures Dataset (DTD), University f Oxfrd, England Surce URL: https://www.rbts.x.ac.uk/~vgg/data/dtd/ Original Authr: Mircea Cimpi, Subhransu354Maji, Iasnas Kkkins, Sammy Mhamed, Andrea Vedaldi Original cntact: {mircea, vedaldi}@rbts.x.ac.uk Meta Album authr: Ihsan Ullah Created Date: 01 March 2022 Cntact Name: Ihsan Ullah Cntact Email: meta-album@chalearn.rg Cntact URL: [https://meta-album.github.i/](https://meta-album.github.i/) ### Cite this dataset ``` @InPrceedings{cimpi14describing, Authr = {M. Cimpi and S. Maji and I. Kkkins and S. Mhamed and and A. Vedaldi}, Title = {Describing Textures in the Wild}, Bktitle = {Prceedings f the {IEEE} Cnf. n Cmputer Visin and Pattern Recgnitin ({CVPR})}, Year = {2014} } ``` ### Cite Meta-Album ``` @inprceedings{meta-album-2022, title={Meta-Album: Multi-dmain Meta-Dataset fr Few-Sht Image Classificatin}, authr={Ullah, Ihsan and Carrin, Dustin and Escalera, Sergi and Guyn, Isabelle M and Huisman, Mike and Mhr, Felix and van Rijn, Jan N and Sun, Hazhe and Vanschren, Jaquin and Vu, Phan Anh}, bktitle={Thirty-sixth Cnference n Neural Infrmatin Prcessing Systems Datasets and Benchmarks Track}, url = {https://meta-album.github.i/}, year = {2022} } ``` ### Mre Fr mre infrmatin n the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.i/paper/Meta-Album.pdf) Fr details n the dataset preprcessing, please see the [[supplementary materials]](https://penreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) Supprting cde can be fund n ur [[GitHub rep]](https://github.cm/ihsaan-ullah/meta-album) Meta-Album n Papers with Cde [[Meta-Album]](https://paperswithcde.cm/dataset/meta-album) ### Other versins f this dataset [[Mini]](https://www.penml.rg/d/44294) [[Extended]](https://www.penml.rg/d/44328)

9 features

class (target)nominal2 unique values
0 missing
pregnumeric17 unique values
0 missing
plasnumeric136 unique values
0 missing
presnumeric47 unique values
0 missing
skinnumeric51 unique values
0 missing
insunumeric186 unique values
0 missing
massnumeric248 unique values
0 missing
pedinumeric517 unique values
0 missing
agenumeric52 unique values
0 missing

107 properties

768
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
2
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.
8
Number of numeric attributes.
1
Number of nominal attributes.
0.55
Average class difference between consecutive instances.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.27
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.93
Entropy of the target attribute values.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.01
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
65.1
Percentage of instances belonging to the most frequent class.
500
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
7.21
Maximum kurtosis among attributes of the numeric type.
120.89
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
2.27
Maximum skewness among attributes of the numeric type.
115.24
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
2.78
Mean kurtosis among attributes of the numeric type.
44.99
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Average number of distinct values among the attributes of the nominal type.
0.53
Mean skewness among attributes of the numeric type.
25.73
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.52
Minimum kurtosis among attributes of the numeric type.
0.47
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
-1.84
Minimum skewness among attributes of the numeric type.
0.33
Minimum standard deviation of attributes of the numeric type.
34.9
Percentage of instances belonging to the least frequent class.
268
Number of instances belonging to the least frequent class.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.24
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
11.11
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
88.89
Percentage of numeric attributes.
11.11
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.28
First quartile of kurtosis among attributes of the numeric type.
8.02
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.29
First quartile of skewness among attributes of the numeric type.
4.5
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
1.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
32.62
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.54
Second quartile (Median) of skewness among attributes of the numeric type.
13.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
5.49
Third quartile of kurtosis among attributes of the numeric type.
77.13
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
1.72
Third quartile of skewness among attributes of the numeric type.
28.82
Third quartile of standard deviation of attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

1967 runs - estimation_procedure: 10% Holdout set - target_feature: class
506 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
336 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Define a new task