Datasets:

OfficerChul
/

Android-Control-84k

license: apache-2.0
task_categories:
  - visual-question-answering
  - image-to-text
language:
  - en
tags:
  - mobile-ui
  - gui-grounding
  - android
  - ui-automation
  - multimodal
size_categories:
  - 10K<n<100K
pretty_name: Android Control Dataset for LLaMA-Factory

Android Control Dataset

Overview

This directory contains two dataset files (and_ctrl_train.json and and_ctrl_test.json) derived from the Android Control project by Google Research. These datasets have been formatted specifically for GUI grounding training in LLaMA-Factory.

Dataset Description

The Android Control dataset consists of episodes where each episode contains multiple steps. Each step includes:

Step instructions: Natural language instructions for UI interactions
Actions: The type of action to perform (click, scroll, input text, etc.)
Coordinates: Precise x, y coordinates for the action

The data has been extracted and formatted to train models for mobile UI understanding and interaction tasks.

Files

and_ctrl_train.json: Training dataset
and_ctrl_test.json: Test/evaluation dataset
download_android_control.ipynb: Jupyter notebook for downloading images and processing the original data

Data Format

Each entry in the JSON files follows the LLaMA-Factory conversation format:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
      "role": "user",
      "content": "<image>Click on the Recording 2"
    },
    {
      "role": "assistant",
      "content": "{\"action_type\": \"click\", \"x\": 561, \"y\": 535}"
    }
  ],
  "images": ["and_ctrl/out_episode_18557_step_001.png"]
}

Setup Instructions

To use these datasets in LLaMA-Factory:

Create the image directory:
```
mkdir -p data/and_ctrl
```
Download images: Run the provided download_android_control.ipynb notebook to download and process the original images. The notebook will:
- Download TFRecord files from Google Storage (gs://gresearch/android_control/)
- Extract images and save them directly to and_ctrl/ directory
- Automatically organize images with the naming convention: out_episode_{episode_id}_step_{step_number}.png
- Generate an and_ctrl.json file with the processed data
Dataset files:
- Images: Stored in data/and_ctrl/ folder
- Training dataset: and_ctrl_train.json in data/datasets/
- Test dataset: and_ctrl_test.json in data/datasets/

Dataset Statistics

Total samples: Train: 82,944 | Test: 904

Action Type	Train	Test
click	51,793 (62.44%)	125 (13.83%)
scroll	11,005 (13.27%)	125 (13.83%)
input_text	5,966 (7.19%)	125 (13.83%)
wait	5,657 (6.82%)	125 (13.83%)
open_app	5,572 (6.72%)	125 (13.83%)
navigate_back	2,909 (3.51%)	125 (13.83%)
long_press	42 (0.05%)	125 (13.83%)
navigate_home	0 (0.00%)	29 (3.21%)

Note: The training dataset shows a natural distribution with click actions being dominant (62.44%), while the test dataset is intentionally balanced with most action types having equal representation (~13.83% each). The navigate_home action appears only in the test set.

Training Usage

These datasets are specifically formatted for training multimodal language models to:

Understand mobile UI screenshots
Ground natural language instructions to specific UI elements
Generate precise action coordinates for UI automation
Learn mobile app interaction patterns

Source and Attribution

Original dataset: Google Research Android Control

The Android Control dataset was created by Google Research for advancing mobile UI understanding and automation research.

License

This dataset is derived from Google Research's Android Control dataset, which is licensed under the Apache License 2.0. The reformatted version for LLaMA-Factory maintains the same Apache 2.0 license terms.

Copyright for the original dataset belongs to Google LLC. Any modifications or reformatting for LLaMA-Factory compatibility are also provided under Apache License 2.0.

Notes

The images are referenced with relative paths starting with and_ctrl/
Each action includes the action type and necessary parameters (coordinates, text, direction, etc.)
The test set can be used for evaluating model performance on unseen mobile UI interactions