basic docs

docs/source/video_load.mdx

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+# Load video data
+
+<Tip warning={true}>
+
+Video support is experimental and is subject to change.
+
+</Tip>
+
+Video datasets have [`Video`] type columns, which contain `decord` objects. 
+
+<Tip>
+
+To work with video datasets, you need to have the `vision` dependency installed. Check out the [installation](./installation#vision) guide to learn how to install it.
+
+</Tip>
+
+When you load an video dataset and call the video column, the videos are decoded as `decord` Videos:
+
+```py
+>>> from datasets import load_dataset, Video
+
+>>> dataset = load_dataset("path/to/video/folder", split="train")
+>>> dataset[0]["video"]
+<decord.video_reader.VideoReader at 0x1652284c0>
+```
+
+<Tip warning={true}>
+
+Index into an video dataset using the row index first and then the `video` column - `dataset[0]["video"]` - to avoid decoding and resampling all the video objects in the dataset. Otherwise, this can be a slow and time-consuming process if you have a large dataset.
+
+</Tip>
+
+For a guide on how to load any type of dataset, take a look at the <a class="underline decoration-sky-400 decoration-2 font-semibold" href="./loading">general loading guide</a>.
+
+## Local files
+
+You can load a dataset from the video path. Use the [`~Dataset.cast_column`] function to accept a column of video file paths, and decode it into a `decord` video with the [`Video`] feature:
+```py
+>>> from datasets import Dataset, Video
+
+>>> dataset = Dataset.from_dict({"video": ["path/to/video_1", "path/to/video_2", ..., "path/to/video_n"]}).cast_column("video", Video())
+>>> dataset[0]["video"]
+<decord.video_reader.VideoReader at 0x1657d0280>
+```
+
+If you only want to load the underlying path to the video dataset without decoding the video object, set `decode=False` in the [`Video`] feature:
+
+```py
+>>> dataset = dataset.cast_column("video", Video(decode=False))
+>>> dataset[0]["video"]
+{'bytes': None,
+ 'path': 'path/to/video/folder/video0.mp4'}
+```
+
+## VideoFolder
+
+You can also load a dataset with an `VideoFolder` dataset builder which does not require writing a custom dataloader. This makes `VideoFolder` ideal for quickly creating and loading video datasets with several thousand videos for different vision tasks. Your video dataset structure should look like this:
+
+```
+folder/train/dog/golden_retriever.mp4
+folder/train/dog/german_shepherd.mp4
+folder/train/dog/chihuahua.mp4
+
+folder/train/cat/maine_coon.mp4
+folder/train/cat/bengal.mp4
+folder/train/cat/birman.mp4
+```
+
+Load your dataset by specifying `videofolder` and the directory of your dataset in `data_dir`:
+
+```py
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("videofolder", data_dir="/path/to/folder")
+>>> dataset["train"][0]
+{"video": <decord.video_reader.VideoReader at 0x161715e50>, "label": 0}
+
+>>> dataset["train"][-1]
+{"video": <decord.video_reader.VideoReader at 0x16170bd90>, "label": 1}
+```
+
+Load remote datasets from their URLs with the `data_files` parameter:
+
+```py
+>>> dataset = load_dataset("videofolder", data_files="https://foo.bar/videos.zip", split="train")
+```
+
+Some datasets have a metadata file (`metadata.csv`/`metadata.jsonl`) associated with it, containing other information about the data like bounding boxes, text captions, and labels. The metadata is automatically loaded when you call [`load_dataset`] and specify `videofolder`. 
+
+To ignore the information in the metadata file, set `drop_labels=False` in [`load_dataset`], and allow `VideoFolder` to automatically infer the label name from the directory name:
+
+```py
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("videofolder", data_dir="/path/to/folder", drop_labels=False)
+```
+
+## WebDataset
+
+The [WebDataset](https://github.com/webdataset/webdataset) format is based on a folder of TAR archives and is suitable for big video datasets.
+Because of their size, WebDatasets are generally loaded in streaming mode (using `streaming=True`).
+
+You can load a WebDataset like this:
+
+```python
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("webdataset", data_dir="/path/to/folder", streaming=True)
+```