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Datasets:
PrentisAI
/
ScreenRef

Tasks:
Image-Text-to-Text
Modalities:
Image
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
gui-grounding
gui-agent
screenshot
abstention
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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ScreenRef: initial release
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metadata 
pretty_name: ScreenRef
license: other
license_name: restricted-research-only
license_link: >-
  https://huggingface.co/datasets/PrentisAI/ScreenRef-Annotations/blob/main/LICENSE
gated: true
extra_gated_eu_disallowed: true
extra_gated_heading: Restricted access — non-commercial academic research only
extra_gated_description: Access requests are reviewed manually. Please allow 3–5 business days.
extra_gated_prompt: >-
  This dataset consists of screenshots of third-party websites, mobile
  applications, desktop environments, and frames extracted from third-party
  video recordings. Access is governed by the Restricted Research-Only License
  and Terms of Access (the LICENSE, full text:
  https://huggingface.co/datasets/PrentisAI/ScreenRef-Annotations/blob/main/LICENSE).
  If anything below differs from the LICENSE, the LICENSE prevails.


  By requesting access you agree to the LICENSE, and in particular that:


  1. you will use the dataset only for Academic Research as defined in the
  LICENSE: research by an individual affiliated with a university, college, or
  public or non-profit research institution, intended for publication, and not
  carried out for or on behalf of any commercial entity;

  2. you will not redistribute the dataset or any derived copy, except as the
  LICENSE allows (for example, storage on your institution's infrastructure for
  approved users only, or up to ten images in a publication with personal
  information redacted);

  3. you will not attempt to identify, locate, contact or profile any person
  whose personal information appears in the data;

  4. you understand that screenshots were captured automatically and may
  incidentally show personal information; you will not disseminate it, and you
  are asked to report it to requests@prentis.ai;

  5. any model you release that was trained on the dataset will be released only
  under terms that prohibit commercial use, and will cite the dataset;

  6. when notified, you will delete removed material, or all copies on
  termination, within 30 days;

  7. the dataset is provided "as is", without warranty, and the LICENSE is
  governed by the laws of the State of California.
extra_gated_fields:
  Full name: text
  Affiliation (university or public / non-profit research institution): text
  Country: country
  Institutional e-mail: text
  Intended research use: text
  I am affiliated with a university or a public or non-profit research institution, and my use is not for or on behalf of any commercial entity: checkbox
  I have read and agree to the LICENSE (Restricted Research-Only License and Terms of Access): checkbox
  I will not redistribute the data or any derived copy except as the LICENSE allows: checkbox
  I will not attempt re-identification and will not disseminate any personal information I encounter: checkbox
  I will delete data within 30 days when notified: checkbox
extra_gated_button_content: Request access
task_categories:
  - image-text-to-text
language:
  - en
tags:
  - gui-grounding
  - gui-agent
  - screenshot
  - abstention
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

ScreenRef

GUI grounding instructions paired with screenshots from desktop, web and mobile environments, plus an abstention subset in which the referenced UI element is not present and the correct behaviour is to decline to act.

Access to this dataset is gated and granted manually. Use is governed by the LICENSE (Restricted Research-Only License and Terms of Access): Academic Research only, no redistribution, no re-identification. Please read Data provenance before requesting.

The annotations, task recipes, per-image checksums and all scripts are also available without access approval in PrentisAI/ScreenRef-Annotations (no images, 484 MB), so you can inspect the data before requesting access.

Intended use

ScreenRef is intended for Academic Research, as defined in the LICENSE, on GUI agents, in particular:

  • training and fine-tuning models that locate a user-interface element on a desktop, web or mobile screenshot from a natural-language instruction (GUI grounding);
  • studying abstention, i.e. declining to act when the referenced element is not on screen;
  • analysing grounding behaviour across platforms, screen resolutions and instruction types.

Out of scope: any commercial use; identifying, profiling or tracking people or accounts that appear in the screenshots; and tasks beyond single-step grounding, such as multi-step planning, typing or scrolling, which the dataset does not contain.

Composition

Subset Instructions Unique screenshots Notes
Crawl_* (web / Android / Ubuntu) 284,531 237,143 screenshots captured by our own pipeline
traj_* (video-derived frames) 326,363 326,363 1 instruction per frame
*_refusal (abstention) 22,475 22,475 correct answer is abstain
Total 633,369 585,981 447 shards, ~327 GiB

Screenshot totals by source tag:

Tag Screenshots Tag Screenshots
Crawl_Ubuntu 100,761 traj_parallel 170,983
Crawl_1M_filter 92,919 traj_mr 76,799
Crawl_Android_part1 43,463 traj_hr 65,858
refusal_parallel 16,872 traj_endfix 12,723
refusal_hr 4,095 refusal_endfix 1,508

58 task groups in total. Because some screenshots carry several instructions, there are 8.1% more rows than unique images (all re-use is within Crawl_*; traj_* and the refusal subset are strictly 1 : 1).

Schema

Column Type Description
image Image() the screenshot
width, height int32 native resolution of the screenshot
conversations string JSON list of {"from": "human"/"gpt", "value": ...}
task string task group the row belongs to
conv_style string conversation template identifier

conversations is stored as a raw JSON string and is not re-interpreted by the loader. Coordinates are integers normalized to a 1000 × 1000 space, not pixels — for tool-call coordinate and <box> alike. To get pixels: x_px = x / 1000 * width, y_px = y / 1000 * height.

Action space

Answers are <tool_call> blocks. The tool name follows the platform and the action distinguishes grounding from abstention:

Platform Tool Grounding action Abstention action
desktop computer_use left_click + coordinate abstain
web browser_use left_click + coordinate abstain
mobile mobile_use click + coordinate abstain

Row counts: left_click 541,793 · click 58,782 · abstain 22,475. The three *_text_box tasks (10,319 rows) answer with <ref>…</ref><box>[[x1,y1,x2,y2]]</box> instead of a tool call. There are no typing, scrolling, dragging or keyboard actions.

Example of an abstention row:

{"from": "human", "value": "<image>\nLocate and click the \"Window\" menu item."}
{"from": "gpt",   "value": "<tool_call>\n{\"name\": \"computer_use\", \"arguments\": {\"action\": \"abstain\"}}\n</tool_call>"}

Usage 

from datasets import load_dataset

ds = load_dataset("PrentisAI/ScreenRef", split="train", streaming=True)  # requires access + token
row = next(iter(ds))
row["image"]          # PIL image
row["task"]           # e.g. "traj_desktop_refusal__hr"

Every shard holds rows from exactly one task, and shard_index.tsv lists the task, row count and size of each shard. To work with a subset, download only its shards instead of filtering the full 327 GiB. For example, abstention rows only:

import csv
from datasets import load_dataset

# get shard_index.tsv first, e.g. hf_hub_download("PrentisAI/ScreenRef", "shard_index.tsv", repo_type="dataset")
rows = csv.DictReader(open("shard_index.tsv"), delimiter="\t")
shards = [r["shard"] for r in rows if "refusal" in r["task"]]   # 12 shards, 5.8 GiB

refusal = load_dataset("PrentisAI/ScreenRef", data_files=shards, split="train")  # 22,475 rows

Data provenance

The two halves of the dataset were obtained differently, but in both cases the content shown on screen belongs to third parties, not to the maintainers. Specifically:

  • Crawl_* — screenshots collected by the maintainers with their own automated pipeline: publicly reachable web pages rendered in a browser, Android applications running in an emulator (1080×2400) and Ubuntu desktop applications running in a virtual machine (1920×1080). They are not copied from any existing public dataset. Crawl_1M_filter covers a long tail of websites (577 distinct domains appear in the annotation text alone, 72% of them only once, spanning .com/.io/.org/.gov/.de/.ru/.dev/.edu and others).
  • traj_* and the refusal subset — frames extracted from publicly available YouTube tutorial videos of software being operated. The directory name in each image path is the source video's YouTube ID.

Rights in the depicted websites, applications, user interfaces and video recordings remain with their respective owners. The maintainers make no claim of ownership over that content. Prentis AI owns only the annotations and the compilation; the LICENSE licenses those and sets the terms of access for the screenshots.

Takedown / rights requests: if you are a rights holder and wish content removed, contact the maintainers (see Contact). Removal requests are actioned and a corrected revision published; access may be suspended meanwhile.

Because screenshots were captured automatically, some may incidentally show personal information. Please report any you encounter to the address under Contact.

Known limitations

  • Instruction verb and action can disagree. Every grounding answer is a single click, but about 3.6% of the video-derived instructions name a different gesture — right-click (5,960), double-click (3,659), hover (1,650), drag (277) or long-press (223). Treat these as location supervision only; the dataset does not teach gesture selection.
  • Coordinates are normalized to 0–1000 on both axes, while resolutions are heterogeneous (1080×2400, 1920×1080, 2560×1440, 3840×2160 and others); convert with width / height if you need pixels.

Licence

See the LICENSE (the same file is included here and, publicly, in the annotation repository). In short: Academic Research only (as defined there), no commercial use, no redistribution beyond the listed exceptions, no re-identification, and deletion within 30 days of notice. Models trained on the dataset may be released under non-commercial terms. The LICENSE grants no rights in the depicted software, web content or recorded material.

Contact

Access review, personal-information reports and takedown requests: requests@prentis.ai

Citation

The accompanying paper is currently under review. Citation information will be added upon publication.