DEVELOPMENT... { "data_id": "40926", "name": "CIFAR_10_small", "exact_name": "CIFAR_10_small", "version": 1, "version_label": null, "description": "**Author**: Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton \r\n**Source**: [University of Toronto](https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html) - 2009 \r\n**Please cite**: Alex Krizhevsky (2009) Learning Multiple Layers of Features from Tiny Images, Tech Report.\r\n\r\n**CIFAR-10 small** \r\nThis is a 20,000 instance sample of the original CIFAR-10 dataset. Sampled randomly and stratified, with 2000 examples per class. Training and test set are merged. Find the corresponding task for the original train-test splits.\r\n\r\nCIFAR-10 is a labeled subset of the [80 million tiny images dataset](http:\/\/groups.csail.mit.edu\/vision\/TinyImages\/). It (originally) consists 32x32 color images representing 10 classes of objects: \r\n0. airplane \r\n1. automobile \r\n2. bird \r\n3. cat \r\n4. deer \r\n5. dog \r\n6. frog \r\n7. horse \r\n8. ship \r\n9. truck \r\n\r\nThe classes are completely mutually exclusive. There is no overlap between automobiles and trucks. \"Automobile\" includes sedans, SUVs, things of that sort. \"Truck\" includes only big trucks. Neither includes pickup trucks.\r\n\r\nThe original CIFAR-10 dataset contains 6000 images per class. The original train-test split randomly divided these into 5000 train and 1000 test images per class.\r\n\r\n### Attribute description \r\n\r\nEach instance represents a 32x32 colour image as a 3072-value array. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.\r\n\r\nThe labels are encoded as integers in the range 0-9, corresponding to the numbered classes listed above", "format": "ARFF", "uploader": "Felicia West", "uploader_id": 2, "visibility": "public", "creator": null, "contributor": null, "date": "2017-09-26 23:24:38", "update_comment": null, "last_update": "2017-09-26 23:24:38", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/16797612\/cifar-10-small.arff", "default_target_attribute": "class", "row_id_attribute": null, "ignore_attribute": null, "runs": 380, "suggest": { "input": [ "CIFAR_10_small", "This is a 20,000 instance sample of the original CIFAR-10 dataset. Sampled randomly and stratified, with 2000 examples per class. Training and test set are merged. Find the corresponding task for the original train-test splits. CIFAR-10 is a labeled subset of the [80 million tiny images dataset](http:\/\/groups.csail.mit.edu\/vision\/TinyImages\/). It (originally) consists 32x32 color images representing 10 classes of objects: 0. airplane 1. automobile 2. bird 3. cat 4. deer 5. dog 6. frog 7. horse 8 " ], "weight": 5 }, "qualities": { "NumberOfInstances": 20000, "NumberOfFeatures": 3073, "NumberOfClasses": 10, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 3072, "NumberOfSymbolicFeatures": 1, "Quartile1MutualInformation": null, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "PercentageOfMissingValues": 0, "PercentageOfNumericFeatures": 99.96745850959974, "PercentageOfSymbolicFeatures": 0.03254149040026033, "Quartile1AttributeEntropy": null, "Quartile1KurtosisOfNumericAtts": -0.8530516520838605, "Quartile1MeansOfNumericAtts": 114.49035000000003, "StdvNominalAttDistinctValues": 0, "Quartile1SkewnessOfNumericAtts": 0.08522476280202099, "Quartile1StdDevOfNumericAtts": 60.07322284203119, "Quartile2AttributeEntropy": null, "Quartile2KurtosisOfNumericAtts": 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