Created a dataset card

README.md

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+---
+language:
+- en
+license: agpl-3.0
+tags:
+- video
+- soccernet
+- football
+- soccer
+- sport
+- action-anticipation
+size_categories: # Number of clips
+- 10K<n<100K
+source_datasets:
+- SoccerNet/SN-BAS-2024
+# HuggingFace does not have a task category for action anticipation. Therefore, I had to remove the task_categories tag
+#task_categories:
+#- action-anticipation
+---
+
+<!-- Builds from the template https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1 -->
+
+# SoccerNet Challenge 2026 - Action Anticipation Dataset
+
+<!-- Provide a quick summary of the dataset. -->
+
+The SoccerNet Action Anticipation dataset splits the [2024 ball action spotting dataset](https://huggingface.co/datasets/SoccerNet/SN-BAS-2024) 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
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/MohamadDalal/FAANTRA
+- **Paper:** https://huggingface.co/papers/2504.12021
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+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.
+
+## Download
+
+The setup_dataset_BAA.py script inside the FAANTRA repository can be used to download and setup the dataset.
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+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
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**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}
+}
+```