DeskForge-1M

DeskForge-1M is a corpus of 1.21M annotated desktop screenshots with 159.7M element instances and 917K recorded click transitions, generated with DeskForge, a controllable desktop environment that composes and explores real applications.

Project page · Code · Model · Paper (coming soon)

A. Said Gurbuz · Ahmed Nassar · Sunghwan Hong · Marc Pollefeys · Peter W. J. Staar
ETH Zurich · IBM Research Zurich · Microsoft

Every screenshot is annotated densely: not one target, but every visible element, with its type, text, geometry, interaction properties, hierarchy and owning window. Overlapping windows are resolved, so each element records both its full extent and the fragments that are actually visible. Scenes combine several real applications with varied content, window layouts, appearance presets and display resolutions, and short click explorations link each action to the screen before and after it.

At a glance

Observations	1,207,368 screenshots in 323,731 scenes
Element instances	159.7M (132 per screen on average)
Click transitions	917,211, from 135,731 exploration episodes
Instructions	663,635 clicks with natural-language instructions
Applications	19 real Linux desktop applications, several per screen
Appearance	7 presets, from classic Linux to Windows- and macOS-inspired styles
Resolutions	7, from 1366×768 to 3840×2160

Splits

Splits are made per scene, so all frames of a scene stay together. Three test splits hold an attribute out of training entirely, which measures generalization to desktop configurations never seen in training.

split	held out	observations	transitions	instructions
train		999,494	760,850	551,651
val		11,279	8,667	6,281
test_id	new scenes with seen attributes	22,550	17,383	12,589
test_app	GNOME System Monitor, Pluma, Xarchiver	43,206	32,848	22,729
test_theme	the Quartz Night Nord preset	51,627	38,671	27,175
test_resolution	2880×1800	79,212	58,792	43,210

A held-out application is excluded from every scene that contains it, in any frame. See docs/splits.md for how each axis was chosen.

What a sample contains

The default configuration streams the observations as WebDataset shards. Each observation is four members sharing one key:

member	content
png	the screenshot
leaf.json	every visible element: type, role, name and visible text, rect and visible_fragments, interaction state, parent and reading order, owning application and window
screentag.txt	the screen serialized as ScreenTag markup, on a 0–500 grid normalized to the screenshot
record.json	scene metadata (applications, preset, resolution, window stack), eligibility flags, and the action that produced this state

Alongside the shards, index/ holds Parquet tables for observations, transitions, instructions, episodes and scenes: index/transitions/<split>.parquet lists every recorded click with its before and after observation keys, target element and effect, and index/instructions/<split>.parquet gives the clicks their natural-language instructions. demo/ holds browsing samples: stratified observations with images inline (downscaled to 1024 px) and the transitions_preview shards below. The field reference is in docs/schema.md.

Browsing transitions

The transitions_preview subset shows recorded clicks in the Dataset Viewer: 100 per split, each from a different episode, varied over applications, element types, appearance presets and resolutions. A row reads as one step:

member	content
instruction.txt	a natural-language instruction for the click
before.png	the screen the click was taken on
target.png	the clicked element, cropped from the before screen and outlined
after.png	the screen the click produced
action.txt	the click, its target element and application
transition.json	instruction variants, action and target geometry, effect, scene

Screenshots are byte-identical copies of the corpus members. The subset is for browsing; all transitions and instructions are in index/ and the shards.

Instructions

663,635 recorded clicks come with natural-language instructions, synthesized with Qwen3.6-27B from the click, its target and the screens before and after it. An instruction states one coordinate-free goal that can be carried out from the before screen, in a standard and a more detailed_contextual style; primary_instruction is the standard one where it exists, and referring_expression describes the target on the before screen. The 551,651 training instructions are the pool for grounding training, and the 105,703 in the four test splits are the grounding evaluation examples.

Loading

from datasets import load_dataset

# Stream observations from any split.
ds = load_dataset("docling-project/DeskForge-1M", split="test_app", streaming=True)
sample = next(iter(ds))
sample["png"], sample["leaf.json"], sample["screentag.txt"], sample["record.json"]

# Browse recorded clicks: instruction, before, clicked element, after.
preview = load_dataset("docling-project/DeskForge-1M", "transitions_preview", split="val")

# Transitions: before/after keys, the clicked target and what changed.
transitions = load_dataset(
    "parquet",
    data_files="hf://datasets/docling-project/DeskForge-1M/index/transitions/val.parquet",
    split="train",
)

# Instructions: one row per click, joined to transitions by transition_id.
instructions = load_dataset(
    "parquet",
    data_files="hf://datasets/docling-project/DeskForge-1M/index/instructions/val.parquet",
    split="train",
)

For full passes, read the shards with webdataset; the Parquet indexes select subsets before streaming, and give the byte offset of every member for random access:

import webdataset as wds
ds = (wds.WebDataset("data/train/part-{00000..00903}.tar", shardshuffle=True)
        .decode("pil")
        .to_tuple("png", "leaf.json", "screentag.txt", "record.json"))

import pyarrow.parquet as pq
obs = pq.read_table("index/observations/test_id.parquet")
heavily_occluded = obs.filter(obs["occluded_ratio"] > 0.5)

examples/ contains runnable scripts for streaming states, pairing transitions and fetching a single observation by key.

Recommended filters. state_train_eligible selects the still-image view (excluding near-duplicate scenes and frames where a click changed nothing), and transition_train_eligible selects the action view, which keeps no-op clicks as supervision.

Annotation quality

Annotations are generated automatically from the accessibility tree and reconciled with the screenshot and the window stack. Captures that fail the automated audits (element coverage against rendered pixels, blank-widget suppression, window ownership and fragment containment) are not included. A human audit finds 99.8% of sampled element annotations correct and 97.8% of sampled instructions sound.

Limitations

All screenshots come from one Linux backend (Xfce): the Windows- and macOS-inspired presets reproduce the look of those systems rather than run them. Actions are clicks from undirected exploration, not goal-directed demonstrations; instructions are written for single clicks after the fact. Because annotations transcribe what is on screen, text drawn by applications (for example session paths or live web content in browser scenes) is part of the data.

License

The dataset is released under the MIT License. Application interfaces, themes, icons, wallpapers and web content visible in the screenshots remain the property of their respective owners.

Citation

@article{gurbuz2026deskforge,
  title   = {DeskForge: Dense Supervision from Desktop Environments
             for Computer-Use Agents},
  author  = {Gurbuz, A. Said and Nassar, Ahmed and Hong, Sunghwan and
             Pollefeys, Marc and Staar, Peter W. J.},
  journal = {arXiv preprint},
  year    = {2026}
}