Datasets:

leosltl
/

Android-in-the-Wild

license: cc-by-4.0
task_categories:
  - image-classification
  - visual-question-answering
tags:
  - android
  - mobile
  - ui-automation
  - screen-understanding
pretty_name: Android in the Wild (AITW)
size_categories:
  - 100M<n<1B

Android in the Wild (AITW)

This is a mirror of Google's Android in the Wild (AITW) dataset, re-hosted on Hugging Face for easier community access.

Original Source

Paper: Android in the Wild: A Large-Scale Dataset for Android Device Control
Original Repository: google-research/google-research/tree/master/android_in_the_wild

Dataset Description

Android in the Wild (AITW) is a large-scale dataset for Android device control. It contains human demonstrations of natural language instructions being carried out on Android devices. Each demonstration consists of a sequence of screenshots paired with corresponding actions (taps, swipes, types, etc.) and UI annotations.

Dataset Structure

The dataset is organized into 5 subsets, stored as gzip-compressed TFRecord files:

Subset	Shards	Description
`general`	321	General Android tasks
`google_apps`	8,688	Tasks on Google applications
`install`	1,052	App installation tasks
`single`	252	Single-step tasks
`web_shopping`	1,025	Web shopping tasks

Additionally, the splits/ directory contains JSON files defining train/test splits:

standard.json
unseen_android_version.json
unseen_domain.json
unseen_subject.json
unseen_verb.json

Data Format

Each TFRecord contains examples with the following fields:

image/encoded — screenshot image (encoded)
image/ui_annotations_ui_types — UI element type annotations (e.g., ICON_STOP, ICON_V_BACKWARD)
Additional action and metadata fields

Usage

import tensorflow as tf
import gzip

def read_tfrecord(file_path):
    with gzip.open(file_path, 'rb') as f:
        raw = f.read()
    dataset = tf.data.TFRecordDataset([file_path], compression_type='GZIP')
    return dataset

For detailed usage instructions, refer to the original repository.

License

This dataset is licensed under CC BY 4.0, following the original release.

Citation

@article{rawles2023android,
  title={Android in the Wild: A Large-Scale Dataset for Android Device Control},
  author={Rawles, Christopher and Li, Alice and Rodriguez, Daniel and Ber, Oriana and Zitkovich, Brianna},
  journal={arXiv preprint arXiv:2307.10088},
  year={2023}
}

Disclaimer

This is an unofficial mirror. All credit goes to the original authors at Google Research. This copy is provided solely to facilitate easier access for the research community.