cua-lite/Jedi

cua-lite preprocessed version of Jedi (xlangai/Jedi). Desktop GUI data covering three task types: understanding (icon captioning, layout description), grounding:bbox (layout regions), grounding:point (icon centers).

Origin

• https://huggingface.co/datasets/xlangai/Jedi

Load via datasets

from datasets import load_dataset

# entire dataset
ds = load_dataset("cua-lite/Jedi")

# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/Jedi", "desktop-grounding-bbox")

You can also filter by metadata.platform / metadata.task_type / metadata.others.* after loading; every row carries a rich metadata struct (see schema below).

Schema

Each row has these columns:

column	type	notes
image_ids	list[string]	content-addressed ids (<sha256>.<ext>), enables cross-parquet / cross-dataset dedup
images	list[Image]	bytes embedded at HF push time; matches image_ids index-for-index
messages	list[struct]	OpenAI-style turns with role + structured content
metadata	struct	{platform, task_type, split, others{...}}

Coordinate values in messages are normalized to [0, 1000] integers.

Layout

<platform>/<task_type>/<split>.parquet                   # single-variant cohort
<platform>/<task_type>/<split>/<variant>.parquet         # multi-variant cohort
<platform>/<task_type>/<split>/shard-NNNNN-of-NNNNN.parquet            # + sharded single-variant
<platform>/<task_type>/<split>/<variant>/shard-NNNNN-of-NNNNN.parquet  # + sharded multi-variant

• platform ∈ {desktop, mobile, web}
• task_type directory uses a hyphen where the metadata value uses a colon: grounding-action/ → grounding:action
• split ∈ {train, validation} — validation is an in-distribution held-out slice (never used in training); test is reserved for out-of-distribution benchmark datasets

Stats

platform	task_type	variant	train	validation
desktop	grounding:bbox	bbox	1,966,349	2,000
desktop	grounding:point	point	178,297	2,000
desktop	understanding	icon_caption	379,462	2,000
desktop	understanding	layout	849,696	2,000

Image storage

Images are content-addressed by SHA-256 and deduplicated within this repo. The images column on HuggingFace embeds raw bytes so the Hub viewer renders thumbnails and datasets.load_dataset works out of the box.

For local workflows (SFT export, cross-dataset dedup, split rebalancing), run reverse.py on a cloned repo: it extracts each unique image_id once to a shared image_store/<hash[:2]>/<hash>.<ext> and rewrites the parquets to drop the images column, so rows reference images by hash id only. The shared store is reusable across datasets — the same image in two repos lands in one file.

• Total unique images: 431,898
• Store size: 58.18 GB

Notes

(none)

License & citation

See original dataset (xlangai/Jedi)

See https://huggingface.co/datasets/xlangai/Jedi