README.md

---
dataset_info:
  features:
  - name: video_id
    dtype: string
  - name: title
    dtype: string
  - name: os
    dtype: string
  - name: num_scenes
    dtype: int64
  - name: scene_timestamps_in_sec
    sequence: float64
  - name: screen_bboxes
    sequence:
      sequence: int64
  - name: ui_element_bboxes
    sequence:
      sequence:
        sequence: float64
  - name: raw_actions
    list:
      list:
      - name: box_id
        dtype: int64
      - name: details
        dtype: string
      - name: type
        dtype: string
  - name: actions
    list:
      list:
      - name: action_type_id
        dtype: int64
      - name: action_type_text
        dtype: string
      - name: annot_position
        sequence: float64
      - name: lift
        sequence: float64
      - name: touch
        sequence: float64
      - name: type_text
        dtype: string
  - name: video_fps
    dtype: float64
  - name: video_width
    dtype: int64
  - name: video_height
    dtype: int64
  splits:
  - name: train
    num_bytes: 69622260
    num_examples: 19725
  - name: validation
    num_bytes: 1641036
    num_examples: 495
  - name: test
    num_bytes: 565401
    num_examples: 100
  - name: test_unseen_os
    num_bytes: 169823
    num_examples: 50
  download_size: 18085770
  dataset_size: 71998520
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
  - split: test_unseen_os
    path: data/test_unseen_os-*
---

[Paper](https://arxiv.org/abs/2505.12632) |
[Code](https://github.com/runamu/monday) |
[Dataset](https://huggingface.co/datasets/runamu/MONDAY) |
[Project](https://monday-dataset.github.io)

# Dataset Card for MONDAY

_MONDAY_ (Mobile OS Navigation Task Dataset for Agents from YouTube) is a cross-platform mobile navigation dataset for training vision-language models. This dataset contains
- **20K** curated list of videos of mobile navigation tasks from YouTube, including Android and iOS devices.
- **333K** detected scenes, each representing a temporally segmented step within a mobile navigation task.
- **313K** identified actions, including touch, scroll, hardware, typing, long press, multi touch and zoom.

Please visit our [project page](https://monday-dataset.github.io/) for more details.

## Data Fields
- **video_id (str)**: Unique identifier for the video.
- **title (str)**: Title of the video.
- **os (str)**: Operating system of the mobile device used in the video.
- **num_scenes (int)**: Number of detected scenes in the video.
- **scene_timestamps_in_sec (list)**: A list of timestamps of the detected scenes in seconds. The list has a length of `num_scenes`.
- **screen_bboxes (list)**: A list of bounding boxes for the detected phone screen in each scene, given as (left, top, right, bottom) pixel coordinates. The list has a length of `num_scenes`.
- **ui_element_bboxes (list)**: A list of bounding boxes for the detected user interface (UI) elements in each scene, given as (left, top, right, bottom) coordinates normalized to the [0, 1] range. The list has a length of `num_scenes - 1`.
  ```python
  # example
  ui_element_bboxes = [
      [ui_bbox1_scene1, ui_bbox2_scene1, ...], # UI elements in scene 1
      [ui_bbox1_scene2, ui_bbox2_scene2, ...], # UI elements in scene 2
      ...
  ]
  ```
- **raw_actions (list)**: A list of raw actions identified from the video for each scene. The list has a length of `num_scenes - 1`. Multiple actions can be annotated within a single scene, and all are considered valid. Each element is a list of actions annotated in that scene, with each action represented as a dictionary containing the following keys:
  - **box_id (int)**: The index of the UI element's bounding box (from `ui_element_bboxes[scene_id]`) associated with the action. If the action does not correspond to any UI element, the value is -1.
  - **details (str)**: A detailed description of the action, either automatically generated or manually annotated during the identification process.
  - **type (str)**: A text label describing the action type.  Possible values include `"touch"`, `"scroll"`, `"hardware"`, `"typing"`, `"long press"`, `"multi touch"` and `"zoom"`, listed in order of frequency in the dataset.
  ```python
  # example
  raw_actions = [
    [
        {"box_id": 0, "details": "...", "type": "touch"}, # First action in scene 1
        {"box_id": 1, "details": "...", "type": "touch"}, # Second action in scene 1
    ],
    [
        {"box_id": -1, "details": "...", "type": "typing"}, # First action in scene 2
    ],
    ...
  ]
  ```
  Note: The `box_id` is -1 for actions that do not correspond to any UI element.

- **actions (list)**: A list of actions in each scene, processed for mobile navigation agent training and evaluation. The list has a length of `num_scenes - 1`. Multiple actions can be annotated within a single scene, and all are considered valid. Each element is a list of actions annotated in that scene, with each action represented as a dictionary containing the following keys:
  - **action_type_id (int)**: An integer identifier for the action type, based on the action type sets used in [SeeClick](https://github.com/njucckevin/SeeClick/blob/main/agent_tasks/action_type.py) and [AitW](https://github.com/google-research/google-research/blob/master/android_in_the_wild/action_type.py).
  - **action_type_text (str)**: A text label describing the action type.  Possible values include `"click"`, `"scroll down"`, `"press home"`, `"type"`, `"scroll up"`, `"other hardware"`, `"scroll left"`, `"zoom or multi-touch"`, `"press power"`, `"scroll right"`, and `"press back"`, listed in order of frequency in the dataset.
  - **annot_position (array)**: A flattened array of bounding box coordinates for detected UI elements, formatted as (top, left, height, width), normalized to the [0, 1] range, and rounded to three decimal places. If applicable, the length of this array is `4 * num_ui_elements` per scene; otherwise, it is an empty list.
  - **lift (array)**: Lift coordinates in (x, y) format, normalized to the [0, 1] range and rounded to three decimal places. If not applicable, the value is (-1, -1).
  - **touch (array)**: Touch coordinates in (x, y) format, normalized to the [0, 1] range and rounded to three decimal places. If not applicable, the value is (-1, -1).
  - **type_text (str)**: The entered text, if the action type is `"type"`; otherwise, this is an empty string.
  ```python
  # example
  actions = [
    [
        {"action_type_id": 4, "action_type_text": "click", "annot_position": annot_position, "lift": lift_point_action1, "touch": touch_point_action1, "type_text": ""}, # First action in scene 1
        {"action_type_id": 4, "action_type_text": "click", "annot_position": annot_position, "lift": lift_point_action2, "touch": touch_point_action2, "type_text": ""}, # Second action in scene 1
    ],
    [
        {"action_type_id": 3, "action_type_text": "type", "annot_position": [], "lift": [-1, -1], "touch": [-1, -1], "type_text": "..."}, # First action in scene 2
    ],
    ...
  ]
  ```
  Note: The data format of `actions` is derived from [SeeClick](https://github.com/njucckevin/SeeClick/blob/main/agent_tasks/readme_agent.md) and [AitW](https://github.com/google-research/google-research/tree/master/android_in_the_wild#dataset-format).

- **video_fps (float)**: Frames per second of the video. _This value must be preserved when downloading the video to ensure consistency with `scene_timestamps_in_sec`._
- **video_width (int)**: Width of the video in pixels. _This value must be preserved when downloading the video to ensure consistency with `screen_bboxes`._
- **video_height (int)**: Height of the video in pixels. _This value must be preserved when downloading the video to ensure consistency with `screen_bboxes`._

## Citation
```bibtex
@inproceedings{jang2025_monday,
    title={{Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents}},
    author={Jang, Yunseok and Song, Yeda and Sohn, Sungryull and Logeswaran, Lajanugen and Luo, Tiange and Kim, Dong-Ki and Bae, Kyunghoon and Lee, Honglak},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2025}
}
````