EvoStruggle
| Entity Passport | |
| Registry ID | hf-dataset--shijia2025--evostruggle |
| License | CC-BY-4.0 |
| Provider | huggingface |
Cite this dataset
Academic & Research Attribution
@misc{hf_dataset__shijia2025__evostruggle,
author = {Shijia2025},
title = {EvoStruggle Dataset},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/shijia2025/evostruggle}},
note = {Accessed via Free2AITools Knowledge Fortress}
} π¬Technical Deep Dive
Full Specifications [+]βΎ
βοΈ Nexus Index V2.0
π¬ Index Insight
FNI V2.0 for EvoStruggle: Semantic (S:50), Authority (A:0), Popularity (P:48), Recency (R:52), Quality (Q:30).
Verification Authority
ποΈ Data Preview
Row-level preview not available for this dataset.
Schema structure is shown in the Field Logic panel when available.
π Explore Full Dataset β𧬠Field Logic
Schema not yet indexed for this dataset.
Dataset Specification
EvoStruggle Dataset
Data of video recordings of manual activities of various people performing manual tasks from a first-person perspective. Activities include origami, card shuffling, tangrams and knot tying.
Please see the paper "EvoStruggle: A Dataset Capturing the Evolution of Struggle across Activities and Skill Levels" for more details. The EvoStruggle Dataset Promo Video is available on YouTube.
A related talk was given by Prof Walterio Mayol - Keynote: From Skill to Struggle at the ICCV 2025 SAUAFG Workshop.
Introduction of the Struggle Determination
Our Definition of Struggle
βTo struggle, as defined in the dictionary (verb), is to experience difficulty and make a great effort in order to do something.β
In this work, struggle is defined as observable difficulty in completing a given activity. It may be characterized by one or more of the following indicators:
- Motor hesitation of the hands
- Repeated attempts
- Prolonged actions
- Body-gesture signs of frustration (e.g., hand and/or head movements)
- Disruptive errors and pauses
Struggle examples in each of the activities:
| 1. Tying Knots | 2. Origami |
|---|---|
| 3. Tangram | 4. Shuffle Cards |
What's new in this dataset?
Over 60 hours video recordings, 2,793 videos, and 5,385 annotated temporal struggle segments from 76 participants.
Evolution of Skill: Five Attempts/Repetitions Each Task.
- Diversity: 18 Tasks Grouped into Four activities--Tying Knots, Origami, Tangram Puzzles, and Shuffling Cards.
| 1. Tasks in Tying Knots | 2. Tasks in Origami |
|---|---|
| 3. Tasks in Tangram | 4. Tasks in Shuffle Cards |
Activities and Tasks
| Activity | Tasks (Index : Name) |
|---|---|
| Origami | 01: Paper Plane Β· 02: Fox Β· 03: Helmet Β· 04: Butterfly |
| Shuffle Cards | 01: Hindu Shuffle Β· 02: Classic Shuffle Β· 03: Ribbon Spread and Wave Β· 04: Long Awesome Shuffle Β· 05: Riffle Shuffle |
| Tangram | 01: Runner Β· 02: Kangaroo Β· 03: Cyclist Β· 04: Microscope |
| Tying Knots | 01: Ashley Bend Β· 02: Blakes Hitch Β· 03: Carrick Bend Β· 04: Double Fishermans Bend Β· 05: Slim Beauty Knot |
Usage of the Data
This section describes the annotation format, video naming convention, and data splits used in the Struggle Temporal Action Localization (Struggle TAL) task.
1. Annotations
The annotation files provide metadata for struggle moments in each video, including:
- Start time of a struggle segment
- End time of a struggle segment
These timestamps indicate when observable struggle occurs during task execution.
2. Video Naming Convention
Each video follows the naming format:
participant_id: two-digit participant identifier (e.g.01)task_index: two-digit index of the task within the corresponding activity (as listed above)attempt_id: repetition number of the task, ranging from01to05
Example:01_03_04 denotes participant 01 performing task 03 (e.g. Helmet, Ribbon Spread and Wave, Cyclist, or Carrick Bend, depending on the activity) on the fourth attempt.
3. Code Release
The official code release for the Struggle Temporal Action Localization task is available at:
- GitHub Repository: StruggleTAL
This repository can be used to reproduce the experimental results reported in the paper.
4. Data Splits
We provide three types of data splits for different training and evaluation settings (see Figure 6 in the paper for a visual overview).
4.1 Activity-Level Generalization (Cross-Domain)
Directory: splits/crossdomain_generalization
Description:
These splits are used for Activity-Level Generalization experiments across the four activities.
Files:
<activity_name>_crossdomain.json<activity_name>_crossdomain_testonvalonly.json(recommended)
Notes:
- JSON files are used to run the experiments
- CSV files provide lists of video metadata
- The
*_testonvalonly.jsonfile contains only test samples from the validation split of the unseen activity
4.2 Task-Level Generalization (In-Domain)
Directory: splits/indomain_generalization
Description:
These splits support Task-Level Generalization experiments within each activity.
Files: <activity_name>_subactivity
4.3 Within-Activity and Separate-Attempts Evaluation
Directory: splits/separate_attempts
Description:
- Within-Activity Evaluation:
Provides baseline Struggle TAL performance within the same activity (vanilla setting). - Separate Attempts Evaluation:
Investigates the effect of multiple attempts on Struggle TAL performance.
File: <activity_name>_sepattempt.json. Use this file to run both evaluation settings.
How to Download
Option 1: Hugging Face Dataset (Recommended)
Hugging Face Dataset - Contains 360p resized videos
Please refer to Downloading Datasets documents on Hugging Face to find the suitable command to download the dataset.
Option 2: Baidu NetDisk / ηΎεΊ¦η½η
Choose between the full 1080p version or the compressed 360p version:
- EvoStruggle_Dataset - Full download with original 1080p video recordings (1.18 TB)
- new_struggle_dataset.tar.gz - Compressed version with 360p resized videos (41.81 GB)
Note: The Baidu NetDisk option provides higher resolution videos compared to the Hugging Face version.
Contributors
Citation to this work
@misc{feng2025evostruggledatasetcapturingevolution,
title={EvoStruggle: A Dataset Capturing the Evolution of Struggle across Activities and Skill Levels},
author={Shijia Feng and Michael Wray and Walterio Mayol-Cuevas},
year={2025},
eprint={2510.01362},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.01362},
}
Social Proof
AI Summary: Based on Hugging Face metadata. Not a recommendation.
π‘οΈ Dataset Transparency Report
Technical metadata sourced from upstream repositories.
π Identity & Source
- id
- hf-dataset--shijia2025--evostruggle
- slug
- shijia2025--evostruggle
- source
- huggingface
- author
- Shijia2025
- license
- CC-BY-4.0
- tags
- task_categories:video-classification, language:en, license:cc-by-4.0, size_categories:10b<n<100b, arxiv:2510.01362, region:us
βοΈ Technical Specs
- architecture
- null
- params billions
- null
- context length
- null
- pipeline tag
π Engagement & Metrics
- downloads
- 19,552
- stars
- 0
- forks
- 0
Data indexed from public sources. Updated daily.