dataset_info:
features:
- name: video_id
dtype: string
- name: frame_number
dtype: string
- name: category
dtype: string
- name: image
dtype: image
- name: graph
struct:
- name: edges
list:
- name: predicate
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: nodes
list:
- name: attributes
struct:
- name: appearance
dtype: string
- name: size
dtype: string
- name: id
dtype: string
- name: label
dtype: string
- name: location
dtype: string
- name: graph_sentence
dtype: string
splits:
- name: training
num_bytes: 486523923
num_examples: 12512
- name: train_calibration
num_bytes: 9893243
num_examples: 256
- name: validation
num_bytes: 117826126
num_examples: 3357
- name: testing
num_bytes: 112245657
num_examples: 2849
- name: validation_dev
num_bytes: 3297153
num_examples: 100
download_size: 710966092
dataset_size: 729786102
configs:
- config_name: default
data_files:
- split: training
path: data/training-*
- split: train_calibration
path: data/train_calibration-*
- split: validation
path: data/validation-*
- split: testing
path: data/testing-*
- split: validation_dev
path: data/validation_dev-*
Frame2KG-YC2
Frame2KG-YC2 is a frame-to-knowledge-graph synthetic dataset derived from YouCook2, a cooking video dataset.
Each example contains an image, source metadata, a structured graph of localised entities and relations, and a short graph-derived sentence.
Splits
The training, validation, and testing splits are the primary dataset splits.
The validation_dev split is a 100-example subsample of validation.
The train_calibration split is a 256-example subsample of training, intended for calibration or quantisation workflows.
Citation
If you use this dataset in your work, please cite the paper:
@inproceedings{watson2026frame2kg,
title = {Frame2KG: A Benchmark and Evaluation Toolkit for Interpretable Frame-to-Graph Generation},
author = {Watson, Lewis N. and Strathearn, Carl and Mitchell, Kenny and Yu, Yanchao},
booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
month = {May},
year = {2026},
pages = {10912--10926},
address = {Palma, Mallorca, Spain},
publisher = {European Language Resources Association (ELRA)},
editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},
doi = {10.63317/4ys6kofrzoc5},
url = {https://doi.org/10.63317/4ys6kofrzoc5}
Notes
Annotations are synthetic and cooking-domain specific, so the dataset should be treated as a controlled benchmark rather than general-purpose visual ground truth. Dataset is provided as is.