Datasets:

e1879
/

showui-web-processed

language:
  - en
license: cc-by-4.0
task_categories:
  - image-classification
tags:
  - ui-grounding
  - web
  - showui
  - processed
source_datasets:
  - showlab/ShowUI-web
size_categories:
  - 10K<n<100K

ShowUI-Web Processed

Flattened, normalized, and scenario-split version of showlab/ShowUI-web.

Each row is a single (instruction, UI element) pair with normalized bounding-box coordinates.

Schema

Column	Type	Description
`sample_id`	string	Unique row identifier (`{row}_{element}`)
`screenshot_id`	string	Groups elements from the same screenshot
`image_relpath`	string	Relative path to the screenshot image
`scenario`	string	Website/domain inferred from the image path
`instruction`	string	Natural-language grounding instruction
`bbox_xyxy`	list[float]	Normalized bounding box `[x1, y1, x2, y2]` in `[0, 1]`
`point_xy`	list[float] or null	Normalized click point `[x, y]`
`element_type`	string or null	UI element type label

Splits

Split	Rows	Strategy
train	majority	Scenario-based holdout
validation	~10% scenarios	Domain holdout
test	~15% scenarios	Domain holdout

Repository Layout

The dataset repo contains both row-level parquet artifacts and image files:

flat.parquet — full flattened table (all rows)
splits/train.parquet — train split
splits/val.parquet — validation split
splits/test.parquet — test split
splits/splits.json — split metadata
images/... — screenshot and UI metadata files

Images

Screenshot images are hosted in the images/ directory of this repository. Use image_relpath to construct the path or fetch individual images on demand:

from huggingface_hub import hf_hub_download
from PIL import Image

path = hf_hub_download(
    repo_id="e1879/showui-web-processed",
    repo_type="dataset",
    filename=f"images/{row['image_relpath']}",
)
img = Image.open(path)

Usage

Load train split via datasets:

from datasets import load_dataset

ds = load_dataset("e1879/showui-web-processed")
print(ds["train"][0]["instruction"])

Or load parquet artifacts directly from the dataset repo:

import pandas as pd
from huggingface_hub import hf_hub_download

train_path = hf_hub_download(
    repo_id="e1879/showui-web-processed",
    repo_type="dataset",
    filename="splits/train.parquet",
)
train_df = pd.read_parquet(train_path)
print(train_df.shape)

Credit

Source dataset: showlab/ShowUI-web