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README.md

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+---
+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](https://arxiv.org/abs/2307.10088)
+- **Original Repository:** [google-research/google-research/tree/master/android_in_the_wild](https://github.com/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
+
+```python
+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](https://github.com/google-research/google-research/tree/master/android_in_the_wild).
+
+## License
+
+This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), following the original release.
+
+## Citation
+
+```bibtex
+@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.