Datasets:

runamu
/

MONDAY

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 | Code | Dataset | Project

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}
}