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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:

https://developers.google.com/youtube/v3/guides/searching_by_topic

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

The files included in this package are:

original/test_partition.txt - this contains the testing partition.
original/train_partition.txt - this contains the training partition.

The format for the training/testing partitions is as follows:
URL<space><CSV of Label Indices>

For example, the following line is a valid input:
https://www.youtube.com/watch?v=UDqivjS-lpI 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 youtube.com 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:

https://developers.google.com/youtube/v3/guides/searching_by_topic

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/sportsX_train.txt & cross-validation/sportsX_test.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: https://github.com/gtoderici/sports-1m-dataset/blob/wiki/ProjectHome.md

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:
http://cs.stanford.edu/people/karpathy/deepvideo/

License
-------

This data set is made available under a Creative Commons License:
  http://creativecommons.org/licenses/by/3.0/

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

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