Manual download instructions: This dataset requires you to
download the source data manually into download_config.manual_dir
(defaults to ~/tensorflow_datasets/downloads/manual/):
Follow the download instructions in https://m-bain.github.io/webvid-dataset/
to get the data. Place the csv files and the video directories in
manual_dir/webvid, such that mp4 files are placed in
manual_dir/webvid/*/*_*/*.mp4.
First directory typically being an arbitrary part directory (for sharded
downloading), second directory is the page directory (two numbers around
underscore), inside of which there is one or more mp4 files.
[null,null,["Last updated 2023-04-06 UTC."],[],[],null,["# webvid\n\n\u003cbr /\u003e\n\n| **Warning:** Manual download required. See instructions below.\n\n- **Description**:\n\nWebVid is a large-scale dataset of short videos with textual descriptions\nsourced from the web. The videos are diverse and rich in their content.\n\nWebVid-10M contains:\n\n10.7M video-caption pairs. 52K total video hours.\n\n- **Homepage** :\n \u003chttps://m-bain.github.io/webvid-dataset/\u003e\n\n- **Source code** :\n [`tfds.datasets.webvid.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/webvid/webvid_dataset_builder.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): Initial release.\n- **Download size** : `Unknown size`\n\n- **Dataset size** : `Unknown size`\n\n- **Manual download instructions** : This dataset requires you to\n download the source data manually into `download_config.manual_dir`\n (defaults to `~/tensorflow_datasets/downloads/manual/`): \n\n Follow the download instructions in \u003chttps://m-bain.github.io/webvid-dataset/\u003e\n to get the data. Place the csv files and the video directories in\n `manual_dir/webvid`, such that mp4 files are placed in\n `manual_dir/webvid/*/*_*/*.mp4`.\n\nFirst directory typically being an arbitrary part directory (for sharded\ndownloading), second directory is the page directory (two numbers around\nunderscore), inside of which there is one or more mp4 files.\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Unknown\n\n- **Splits**:\n\n| Split | Examples |\n|-------|----------|\n\n- **Feature structure**:\n\n FeaturesDict({\n 'caption': Text(shape=(), dtype=string),\n 'id': Text(shape=(), dtype=string),\n 'url': Text(shape=(), dtype=string),\n 'video': Video(Image(shape=(360, 640, 3), dtype=uint8)),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|---------|--------------|---------------------|--------|-------------|\n| | FeaturesDict | | | |\n| caption | Text | | string | |\n| id | Text | | string | |\n| url | Text | | string | |\n| video | Video(Image) | (None, 360, 640, 3) | uint8 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n Missing.\n\n- **Citation**:\n\n @misc{bain2021frozen,\n title={Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval},\n author={Max Bain and Arsha Nagrani and Gül Varol and Andrew Zisserman},\n year={2021},\n eprint={2104.00650},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n }"]]