Datasets:
ArXiv:
License:
| license: apache-2.0 | |
| viewer: false | |
| # GUI Grounding Pre-training Data for OS-ATLAS | |
| This document describes the acquisition of the pre-training data used by OS-ATLAS [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://huggingface.co/papers/2410.23218). | |
| <div align="center"> | |
| [\[🏠Homepage\]](https://osatlas.github.io) [\[💻Code\]](https://github.com/OS-Copilot/OS-Atlas) [\[🚀Quick Start\]](#quick-start) [\[📝Paper\]](https://arxiv.org/abs/2410.23218) [\[🤗Models\]](https://huggingface.co/collections/OS-Copilot/os-atlas-67246e44003a1dfcc5d0d045) [\[🤗ScreenSpot-v2\]](https://huggingface.co/datasets/OS-Copilot/ScreenSpot-v2) | |
| </div> | |
|  | |
| **Notes:** In GUI grounding data, the position of the target element is recorded in the `bbox` key, represented by `[left, top, right, bottom]`. | |
| Each value is a [0, 1] decimal number indicating the ratio of the corresponding position to the width or height of the image. | |
| The data stored in this dataset consists of raw data containing **only** element grounding information. When training a model, you need to use the corresponding prompts to wrap these data. | |
| The data we released is divided into three domains: mobile, desktop and web. | |
| All annotation data is stored in JSON format and each sample contains: | |
| * `img_filename`: the interface screenshot file | |
| * `instruction`: human instruction or referring expression extracted from ally tree or html | |
| * `bbox`: the bounding box of the target element corresponding to instruction | |
| Some data also contains a `data_type`, which records the type of an element in its structured information, if it can be obtained. | |
| *** | |
| ### Mobile data | |
| This part of data is stored under the *mobile_domain* directory. Our mobile grounding data consists of four parts. | |
| #### AMEX | |
| Android Multi-annotation EXpo (AMEX) is a comprehensive, large-scale dataset designed for generalist mobile GUI-control agents [1]. | |
| The annotation data is stored in | |
| -`amex_raw.json` | |
| Due to the single file size limitation of Hugging Face datasets, we stored the Amex images in *zip* format and split them into several sub-files. | |
| - `amex_images_part_aa` | |
| - `amex_images_part_ab` | |
| - `amex_images_part_ac` | |
| You need to first merge these split files back into the original file and then extract the contents. | |
| ``` | |
| cat amex_images_part_* > amex_images.zip | |
| 7z x amex_images.zip -aoa -o/path/to/extract/folder | |
| ``` | |
| #### UIBert | |
| UIBert [2] is a dataset extended from Rico dataset [3] for two tasks: similar UI component retrieval and referring expression component retrieval. | |
| The annotation data is stored in | |
| - `uibert_raw.json` | |
| The UIBert images are stored in | |
| - `UIBert.zip` | |
| #### Widget Captioning and RICOSCA | |
| Widget Captioning data are collected by [4]. | |
| RICOSCA is a dataset automatically labeled using Android VH in [5] | |
| The annotation data is stored in | |
| - `widget_captioning.json` | |
| - `ricosca.json` | |
| The rico images are stored in | |
| - `rico_imgs.zip` | |
| #### Android_world_data | |
| This part of data are sampled from a android environment for building and benchmarking autonomous computer control agents [6]. | |
| The annotation data is stored in | |
| - `aw_mobile.json` | |
| The rico images are stored in | |
| - `mobile_images.zip` | |
| *** | |
| ### Desktop data | |
| This part of data is stored under the *desktop_domain* directory. | |
| All of the desktop grounding data is collected from the real environments of personal computers running different operating systems. Each image is split into multiple sub-images to enhance data diversity. | |
| Our desktop grounding data consists of three parts: Windows, Linux and MacOS. | |
| **The image and annotation data for each operating system are stored in corresponding zip and json files.** | |
| It is worth noting that, due to the large size of the Windows image data, the split files need to be merged before extraction. | |
| ``` | |
| cat windows_image_part_* > windows_images.zip | |
| 7z x windows_images.zip -aoa -o/path/to/extract/folder | |
| ``` | |
| *** | |
| ### Web data | |
| This part of data is stored under the *web_domain* directory. | |
| Our desktop grounding data consists of two parts. | |
| #### Seeclick web data | |
| The web data from SeeClick [7] was crawled from websites provided by Common Crawl, containing more than 270k webpage screenshots and over 3 million webpage elements. | |
| The annotation data is stored in | |
| - `seeclick_web.json` | |
| The images are stored into split files and need to be merged before extraction. | |
| ``` | |
| cat seeclick_web_image_part_* > seeclick_web_images.zip | |
| 7z x seeclick_web_images.zip -aoa -o/path/to/extract/folder | |
| ``` | |
| #### Fineweb_crawled_data | |
| This part of data is crawled from web pages from the latest URLs obtained from FineWeb [8], a cleaned and deduplicated English dataset derived from Common Crawl. | |
| Since this portion of the data contains at least 1.6 million images, we have compressed them into 10 zip files, from `fineweb_3m_s11.zip` to `fineweb_3m_s52.zip`. | |
| Please extract them into the same directory. | |
| As an example, | |
| ``` | |
| 7z x fineweb_3m_s11.zip -aoa -o/same/path/to/extract/fineweb | |
| ``` | |
| The annotation data is stored in | |
| - `fineweb_3m.json` | |
| *** | |
| ### Best practice | |
| During the training of **OS-Atlas-7B**, we randomly sampled predefined prompts to wrap the grounding data. Additionally, we scaled the relative coordinates of each element (in the range [0, 1]) by multiplying them by 1000 before inputting them into the model for training. | |
| Below is an example of a data entry: | |
| ``` | |
| { | |
| "conversations": [ | |
| { | |
| "from": "human", | |
| "value": "<image>\nUsing the provided screenshot, I'll describe webpage elements for you to locate (with bbox).\n<ref>media-maniacs.org</ref>\n<ref>Home</ref>\n<ref>Sitemap</ref>\n<ref>shop you can have what you choose 2012</ref>" | |
| }, | |
| { | |
| "from": "gpt", | |
| "value": "<ref>media-maniacs.org</ref><box>[[70,856,141,871]]</box>\n<ref>Home</ref><box>[[21,935,43,951]]</box>\n<ref>Sitemap</ref><box>[[21,919,52,934]]</box>\n<ref>shop you can have what you choose 2012</ref><box>[[368,839,523,855]]</box>" | |
| } | |
| ] | |
| } | |
| ``` | |
| The prompts we used are stored in `prompts.json`. | |
| *** | |
| **The following are the open-source datasets we used as data sources. We welcome everyone to check the details and cite these sources accordingly！** | |
| [1] [AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents](https://arxiv.org/abs/2407.17490) | |
| [2] [UIBert: Learning Generic Multimodal Representations for UI Understanding](https://arxiv.org/abs/2107.13731) | |
| [3] [Rico: A mobile app dataset for building data-driven design applications](https://dl.acm.org/doi/pdf/10.1145/3126594.3126651) | |
| [4] [Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements](https://arxiv.org/pdf/2010.04295.pdf) | |
| [5] [Mapping Natural Language Instructions to Mobile UI Action Sequences](https://arxiv.org/pdf/2005.03776) | |
| [6] [ANDROIDWORLD: A Dynamic Benchmarking Environment for Autonomous Agents](https://arxiv.org/abs/2405.14573) | |
| [7] [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://arxiv.org/abs/2401.10935) | |
| [8] [The fineweb datasets: Decanting the web for the finest text data at scale](https://arxiv.org/abs/2406.17557) |