![\"CIFAR-10\"](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/3649/media/cifar-10.png\")
The CIFAR-10 data consists of 60,000 32x32 color images in 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images in the official data. We have preserved the train/test split from the original dataset. The provided files are:
\r\ntrain.7z - a folder containing the training images in png format
test.7z - a folder containing the test images in png format
trainLabels.csv - the training labels
To discourage certain forms of cheating (such as hand labeling) we have added 290,000 junk images in the test set. These images are ignored in the scoring. We have also made trivial modifications to the official 10,000 test images to prevent looking them up by file hash. These modifications should not appreciably affect the scoring. You should predict labels for all 300,000 images.
\r\nThe label classes in the dataset are:
\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.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes, with 6000 images per class. It was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
\r\nKaggle is hosting a CIFAR-10 leaderboard for the machine learning community to use for fun and practice. You can see how your approach compares to the latest research methods on Rodrigo Benenson's classification results page.
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Please cite this technical report if you use this dataset: Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "Submissions are evaluated on classification accuracy (the percent of labels that are predicted correctly).
\r\nFor each image in the test set, predict a label for the given id. Your labels must match the official labels exactly {airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck}. Your submission should have a header.
\r\nid,label\r\n1,cat\r\n2,cat\r\n3,cat\r\n4,cat\r\n...\r\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\n
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
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There is no maximum team size.
\r\nYou may submit a maximum of 10 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: 10/18/2013 9:07 PM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 10/18/2014 11:59 PM UTC
\r\n\t\r\n