Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

wit

  • Description:

Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.

Split Examples
'train' 37,046,386
  • Features:
FeaturesDict({
    'attribution_passes_lang_id': tf.bool,
    'caption_alt_text_description': Text(shape=(), dtype=tf.string),
    'caption_attribution_description': Text(shape=(), dtype=tf.string),
    'caption_reference_description': Text(shape=(), dtype=tf.string),
    'context_page_description': Text(shape=(), dtype=tf.string),
    'context_section_description': Text(shape=(), dtype=tf.string),
    'hierarchical_section_title': Text(shape=(), dtype=tf.string),
    'image_url': Text(shape=(), dtype=tf.string),
    'is_main_image': tf.bool,
    'language': Text(shape=(), dtype=tf.string),
    'mime_type': Text(shape=(), dtype=tf.string),
    'original_height': tf.int32,
    'original_width': tf.int32,
    'page_changed_recently': tf.bool,
    'page_title': Text(shape=(), dtype=tf.string),
    'page_url': Text(shape=(), dtype=tf.string),
    'section_title': Text(shape=(), dtype=tf.string),
})
  • Citation:
@article{srinivasan2021wit,
  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
  journal={arXiv preprint arXiv:2103.01913},
  year={2021}
}