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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
index: int64
split: string
template: string
viewport: list<item: int64>
  child 0, item: int64
scale: double
theme: string
img_size: list<item: int64>
  child 0, item: int64
n_elements: int64
elements: list<item: struct<type: string, bbox_xyxy: list<item: double>, text: string>>
  child 0, item: struct<type: string, bbox_xyxy: list<item: double>, text: string>
      child 0, type: string
      child 1, bbox_xyxy: list<item: double>
          child 0, item: double
      child 2, text: string
image: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
label: string
to
{'image': Image(mode=None, decode=True), 'label': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              index: int64
              split: string
              template: string
              viewport: list<item: int64>
                child 0, item: int64
              scale: double
              theme: string
              img_size: list<item: int64>
                child 0, item: int64
              n_elements: int64
              elements: list<item: struct<type: string, bbox_xyxy: list<item: double>, text: string>>
                child 0, item: struct<type: string, bbox_xyxy: list<item: double>, text: string>
                    child 0, type: string
                    child 1, bbox_xyxy: list<item: double>
                        child 0, item: double
                    child 2, text: string
              image: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
              label: string
              to
              {'image': Image(mode=None, decode=True), 'label': Value('string')}
              because column names don't match

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Windows UI Synthetic Dataset (ui_synth_v2)

Description

A synthetic dataset of 3 000 Windows-style UI screenshots with bounding box annotations for interactive UI elements. Designed for training lightweight object detectors to serve as a computer-vision fallback in Windows UI automation agents.

Classes

ID Class Description
0 button Clickable buttons
1 textbox Text input fields
2 checkbox Checkbox controls
3 dropdown Dropdown/combobox selectors
4 icon Clickable icons
5 tab Tab controls
6 menu_item Menu items in sidebars/menus

Generation Method

Screenshots are generated by rendering HTML/CSS templates with Playwright (Chromium). Each template represents a common Windows UI pattern (login form, settings panel, dialog, file explorer, etc.).

Domain randomization includes:

  • Light/dark themes with random accent colors
  • Multiple font families (Segoe UI, Arial, Tahoma, Verdana, Calibri)
  • Variable viewport sizes (1280x720, 1440x900, 1920x1080)
  • Random device scale factors (1.0, 1.25, 1.5)
  • Random text content (English)
  • Post-rendering augmentation: JPEG compression, blur, noise, brightness/contrast jitter

Templates (10): login, settings, table, dialog, sidebar, modal, dropdown_open, tabs, toolbar, form_long.

Bounding boxes are extracted directly from DOM getBoundingClientRect() queries on elements marked with data-ui attributes, ensuring pixel-accurate annotations without manual labeling.

Label Format

Labels are provided in YOLO format: one .txt file per image with lines of class_id cx cy w h (normalized 0-1 coordinates).

Dataset Structure

ui_synth_v2/
β”œβ”€β”€ images/
β”‚   β”œβ”€β”€ train/
β”‚   β”œβ”€β”€ val/
β”‚   └── test/
β”œβ”€β”€ labels/
β”‚   β”œβ”€β”€ train/
β”‚   β”œβ”€β”€ val/
β”‚   └── test/
β”œβ”€β”€ data.yaml
└── meta.jsonl

Splits

Split Images
train 2 400
validation 300
test 300
total 3 000

Intended Use

  • Training UI element detectors for Windows UI automation fallback
  • Research in synthetic data generation for GUI understanding
  • Benchmarking object detection on UI-specific domains

Limitations

  • Synthetic data only β€” may not cover all real-world UI patterns
  • English text only
  • Windows 10/11 style only; does not cover macOS, Linux, or web UIs
  • Simple element classes; does not detect complex composite widgets

License

MIT

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