Datasets:
Improve dataset card: Add task categories, paper link, and GitHub link
#2
by
nielsr
HF Staff
- opened
README.md
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---
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license: mit
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language:
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- en
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tags:
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- behavior
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- motion
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- llm
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- vlm
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- esk
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size_categories:
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- 10K<n<100K
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task_categories:
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- question-answering
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---
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# π EPFL-Smart-Kitchen: Lemonade benchmark
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## π Introduction
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Lemonade consists of <span style="color: orange;">36,521</span> closed-ended QA pairs linked to egocentric video clips, categorized in three groups and six subcategories. <span style="color: orange;">18,857</span> QAs focus on behavior understanding, leveraging the rich ground truth behavior annotations of the EPFL-Smart Kitchen to interrogate models about perceived actions <span style="color: tomato;">(Perception)</span> and reason over unseen behaviors <span style="color: tomato;">(Reasoning)</span>. <span style="color: orange;">8,210</span> QAs involve longer video clips, challenging models in summarization <span style="color: gold;">(Summarization)</span> and session-level inference <span style="color: gold;">(Session properties)</span>. The remaining <span style="color: orange;">9,463</span> QAs leverage the 3D pose estimation data to infer hand shapes, joint angles <span style="color: skyblue;">(Physical attributes)</span>, or trajectory velocities <span style="color: skyblue;">(Kinematics)</span> from visual information.
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## πΎ Content
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The current repository contains all egocentric videos recorded in the EPFL-Smart-Kitchen-30 dataset and the question answer pairs of the Lemonade benchmark. Please refer to the [main GitHub repository](https://github.com/amathislab/EPFL-Smart-Kitchen
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### ποΈ Repository structure
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## π Usage
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The evaluation of the benchmark can be done through the following github repository: [https://github.com/amathislab/lmms-eval-lemonade](https://github.com/amathislab/lmms-eval-lemonade)
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## π Citations
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Please cite our work!
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```
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## β€οΈ Acknowledgments
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Our work was funded by EPFL, Swiss SNF grant (320030-227871), Microsoft Swiss Joint Research Center and a Boehringer Ingelheim Fonds PhD stipend (H.Q.). We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services.
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---
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language:
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- en
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license: mit
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size_categories:
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- 10K<n<100K
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task_categories:
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- question-answering
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- video-text-to-text
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tags:
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- behavior
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- motion
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- llm
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- vlm
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- esk
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---
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# π EPFL-Smart-Kitchen: Lemonade benchmark
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[Paper](https://huggingface.co/papers/2506.01608) | [GitHub](https://github.com/amathislab/EPFL-Smart-Kitchen)
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## π Introduction
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Lemonade consists of <span style="color: orange;">36,521</span> closed-ended QA pairs linked to egocentric video clips, categorized in three groups and six subcategories. <span style="color: orange;">18,857</span> QAs focus on behavior understanding, leveraging the rich ground truth behavior annotations of the EPFL-Smart Kitchen to interrogate models about perceived actions <span style="color: tomato;">(Perception)</span> and reason over unseen behaviors <span style="color: tomato;">(Reasoning)</span>. <span style="color: orange;">8,210</span> QAs involve longer video clips, challenging models in summarization <span style="color: gold;">(Summarization)</span> and session-level inference <span style="color: gold;">(Session properties)</span>. The remaining <span style="color: orange;">9,463</span> QAs leverage the 3D pose estimation data to infer hand shapes, joint angles <span style="color: skyblue;">(Physical attributes)</span>, or trajectory velocities <span style="color: skyblue;">(Kinematics)</span> from visual information.
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## πΎ Content
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The current repository contains all egocentric videos recorded in the EPFL-Smart-Kitchen-30 dataset and the question answer pairs of the Lemonade benchmark. Please refer to the [main GitHub repository](https://github.com/amathislab/EPFL-Smart-Kitchen) to find the other benchmarks and links to download other modalities of the EPFL-Smart-Kitchen-30 dataset.
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### ποΈ Repository structure
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## π Usage
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The evaluation of the benchmark can be done through the following github repository: [https://github.com/amathislab/lmms-eval-lemonade](https://github.com/amathislab/lmms-eval-lemonade)
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## π Citations
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Please cite our work!
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```
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## β€οΈ Acknowledgments
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Our work was funded by EPFL, Swiss SNF grant (320030-227871), Microsoft Swiss Joint Research Center and a Boehringer Ingelheim Fonds PhD stipend (H.Q.). We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services.
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