Update dataset card to AndroidWorld

README.md

@@ -1,219 +1,13 @@
 ---
 license: apache-2.0
 viewer: false
+task_categories:
+- robotics
 ---
 
+This repository contains data related to the [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://huggingface.co/papers/2405.14573) environment.
 
-# 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).
+AndroidWorld is a fully functional Android environment that provides reward signals for 116 programmatic tasks across 20 real-world Android apps.
 
-<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>
-
-![os-atlas](https://github.com/user-attachments/assets/cf2ee020-5e15-4087-9a7e-75cc43662494)
-
-
-**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-4B**, 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>"
-    }
-  ]
-}
-```
-
-**OS-Atlas-7B**
-
-```
-{
-  "conversations": [
-    {
-      "from": "human",
-      "value": "<image>\nUsing the provided screenshot, I'll describe webpage elements for you to locate (with bbox).\n<|object_ref_start|>Facebook<|object_ref_end|>\n<|object_ref_start|>Subscribe<|object_ref_end|>\n<|object_ref_start|>Twitter<|object_ref_end|>\n<|object_ref_start|>Read More<|object_ref_end|>\n<|object_ref_start|>Read More<|object_ref_end|>"
-    },
-    {
-      "from": "gpt",
-      "value": "<|object_ref_start|>Facebook<|object_ref_end|><|box_start|>(4,955),(36,970)<|box_end|>\n<|object_ref_start|>Subscribe<|object_ref_end|><|box_start|>(4,913),(43,932)<|box_end|>\n<|object_ref_start|>Twitter<|object_ref_end|><|box_start|>(39,955),(62,970)<|box_end|>\n<|object_ref_start|>Read More<|object_ref_end|><|box_start|>(30,138),(73,157)<|box_end|>\n<|object_ref_start|>Read More<|object_ref_end|><|box_start|>(30,139),(73,155)<|box_end|>"
-    }
-  ]
-}
-```
-
-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)
+*   **Paper:** [https://huggingface.co/papers/2405.14573](https://huggingface.co/papers/2405.14573)
+*   **Code:** [github.com/google-research/android\_world](github.com/google-research/android_world)