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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 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.

  # 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.

  # 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 and AitW.
  • 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.

  # 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 and AitW.

• 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

@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}
}