--- pretty_name: DeskForge-1M license: mit task_categories: - image-to-text - object-detection language: - en tags: - gui - gui-grounding - screen-parsing - computer-use - desktop - accessibility - webdataset size_categories: - 1METH Zurich · IBM Research Zurich · Microsoft ![DeskForge overview](https://saidgurbuz.github.io/deskforge/assets/img/overview-2400.webp) 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`](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](https://github.com/Saidgurbuz/screenparse) 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/.parquet` lists every recorded click with its before and after observation keys, target element and effect, and `index/instructions/.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`](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 ```python 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`](https://github.com/webdataset/webdataset); the Parquet indexes select subsets before streaming, and give the byte offset of every member for random access: ```python 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/`](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](LICENSE). Application interfaces, themes, icons, wallpapers and web content visible in the screenshots remain the property of their respective owners. ## Citation ```bibtex @misc{gurbuz2026deskforge, title={DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents}, author={A. Said Gurbuz and Ahmed Nassar and Sunghwan Hong and Marc Pollefeys and Peter W. J. Staar}, year={2026}, eprint={2610.02320}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2610.02320}, } ```