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The YouTube Sports-1M Dataset

We provide links to 1,133,158 YouTube videos annotated with 487 sports labels. The annotations were generated automatically using the the YouTube Topics, which has a public API accessible at:

Compression: in order to save space, we compressed all the text files referenced below via gzip. To decompress run gzip -d in order to obtain the original filename that we refer to below.

The files included in this package are:

original/testpartition.txt - this contains the testing partition. original/trainpartition.txt - this contains the training partition.

The format for the training/testing partitions is as follows: URL

For example, the following line is a valid input: 168,169

This assigns labels 168, and 169 to the video found at given URL.

labels.txt - this file contains the human-readable labels for the train/test partitions. The first line in the file is assumed to correspond to label 0 (boomerang), and the last corresponds to index 486 (model aircraft).

sports_mids.txt - this file contains the Machine IDs necessary to retrieve videos from via the topics search API. Each line contains the human-readable class name, and the YouTube topic ID, which may be used to directly retrieve videos for the given class using the API below:

Extra files: cross-validation/all_urls.txt - all URLs and labels bundled together (good starting point if you want to make cross-validation partitions). The format is as explained above.

cross-validation/sportsXtrain.txt & cross-validation/sportsXtest.txt for X having values from 0 to 9. These are partitions for 10-fold cross-validation. Since a video may have more than one label, it may appear both in training and in testing. For example, video ABPsSSS2uY0 appears in fold 0 with class 49 for training and class 26 for testing.

Additional Information

Wiki page:

Citation: @inproceedings{KarpathyCVPR14, title = {Large-scale Video Classification with Convolutional Neural Networks}, author = {Andrej Karpathy and George Toderici and Sanketh Shetty and Thomas Leung and Rahul Sukthankar and Li Fei-Fei}, year = {2014}, booktitle = {CVPR} }

Supplemental materials:


This data set is made available under a Creative Commons License:

Attribution 3.0 Unported (CC BY 3.0) Human-Readable Summary

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