Our data can be loaded from the 🤗 huggingface repo at facebook/action100m-preview where we released 10% of the full Action100M for preview. For examples of loading from local parquet files (from cloned repo) and visualization, see usage.ipynb. The data/hySSAAw4t24.json stored in this repo shows a sample.

from datasets import load_dataset

dataset = load_dataset(
    "parquet",
    data_files=f"hf://datasets/facebook/Action100M-preview/data/*.parquet",
    streaming=True,
)
it = iter(dataset["train"])

sample = next(it)

Each sample loaded above contains all annotations for one video, and it has three fields:

• video_uid (string): YouTube video id of the source video.
• metadata (dict): video-level metadata (title / description / ASR transcript, etc.)
• nodes (list[dict]): annotations for each segments.

Each element in nodes is a temporally localized segment in the hierachical Tree-of-Captions, it contains:

• start, end (float): segment boundaries in seconds within the full video.

• node_id (string): unique id of this segment node.

• parent_id (string or null): id of the parent segment. The root node (corresponding to the entire video) has parent_id = null.

• level (int): depth in the hierarchy. Smaller level is coarser (longer segments); larger level is finer (shorter segments).

• plm_caption (string or null): a caption generated by PLM-3B for this segment.

• plm_action (string or null): a short action label produced by PLM-3B.

• llama3_caption (string or null): middle frame caption produced by LLama-3.2-Vision-11B for leaf nodes.

• gpt (dict or null): main Action100M annotations, available for segments that is not too short:

  • gpt["summary"]["brief"]: one-sentence concise caption of the segment.
  • gpt["summary"]["detailed"]: longer, detailed summarization of the video segment.
  • gpt["action"]["brief"]: short verb phrase naming the step.
  • gpt["action"]["detailed"]: imperative-style instruction describing how the action is done.
  • gpt["action"]["actor"]: who/what performs the action (noun phrase).

Texts shown correspond to brief action description (i.e., gpt["action"]["brief"]).

Action100M is under FAIR Noncommercial Research License, as found in the LICENSE file.

@article{chen2026action100m,
  title={Action100M: A Large-scale Video Action Dataset},
  author={Chen, Delong and Kasarla, Tejaswi and Bang, Yejin and Shukor, Mustafa and Chung, Willy and Yu, Jade and Bolourchi, Allen and Moutakanni, Théo and Fung, Pascale},
  journal={arXiv preprint arXiv:2601.10592},
  year={2026}
}