Complete the information below to request access. The SoccerNet team will review your request and the information you provide. If your request is accepted, a personalized Non-Disclosure Agreement will be sent to the email address associated with your Hugging Face account.

By submitting this request, you confirm that the information supplied is accurate and that you have read and accept the SoccerNet Non-Disclosure Agreement available at https://drive.google.com/file/d/1Efz8yP-baa8CtcMB7SYIID7Bn5sH7sGTB4-u1YdVRf4/view.

SoccerNet Challenge 2026 - Action Anticipation Dataset

The SoccerNet Action Anticipation dataset splits the 2024 ball action spotting dataset into 30 second clips, which can then be used to anticipate between 10 action classes that will happen 5 seconds into the future. This is the dataset used for 2026 SoccerNet Action Anticipation challenge.

Relevant Links

Repository: https://github.com/MohamadDalal/FAANTRA
Paper: https://huggingface.co/papers/2504.12021

Uses

Direct Use

The direct use of this dataset is to participate in the 2026 SoccerNet Action Anticipation challenge. For a quick demo the FAANTRA repository can be used to train and evaluate on the dataset.

Other Use

The dataset can be used for further action anticipation research within the soccer field.

Dataset access and NDA

Request access on this Hugging Face page, complete the requested information, and electronically sign the linked SoccerNet NDA. Access requires manual approval. Approval for other SoccerNet repositories does not automatically grant access to this dataset.

Download

The setup_dataset_BAA.py script inside the FAANTRA repository can be used to download and setup the dataset.

Once approved, authenticate with the same Hugging Face account before downloading:

pip install --upgrade huggingface_hub
hf auth login
hf download SoccerNet/ActionAnticipation 224p/valid.zip --repo-type dataset --local-dir SoccerNet/ActionAnticipation

Choose the archive paths for the resolution and splits you need.

Dataset Structure

The dataset is structured as:

224p
|_train.zip
|_valid.zip
|_test.zip
|_challenge.zip
720p
|_train.zip
|_valid.zip
|_test.zip
|_challenge.zip

Each zip file contains a split at a specific resolution. Each split is structured as:

split
|_clip_1
  |_{224p|720p}.mp4
|_clip_2
  |_{224p|720p}.mp4
...
|_Labels-ball.json

The challenge split however, does not contain annotations, and therefore does not have the Labels-ball.json file.

Citation

BibTeX:

@InProceedings{Dalal_2025_CVPR,
    author    = {Dalal, Mohamad and Xarles, Artur and Cioppa, Anthony and Giancola, Silvio and Van Droogenbroeck, Marc and Ghanem, Bernard and Clap\'es, Albert and Escalera, Sergio and Moeslund, Thomas B.},
    title     = {Action Anticipation from SoccerNet Football Video Broadcasts},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2025},
    pages     = {6126-6137}
}

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Paper for SoccerNet/ActionAnticipation

Action Anticipation from SoccerNet Football Video Broadcasts

Paper • 2504.12021 • Published Apr 16, 2025