cua-lite/GUIOdyssey

cua-lite preprocessed version of GUIOdyssey (hflqf88888/GUIOdyssey). A long-horizon cross-app Android mobile dataset of 8,334 task trajectories over ~128k screenshots. Produces two cohorts: navigation (multi-step agent episodes) and understanding (per-step screen captioning from the source description annotations).

Origin

• https://huggingface.co/datasets/hflqf88888/GUIOdyssey

Load via datasets

from datasets import load_dataset

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

# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/GUIOdyssey", "mobile.understanding")

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
images	list[Image]	embedded PNG/JPEG bytes; HF viewer renders thumbnails
messages	list[struct]	OpenAI-style turns with role + structured content
metadata	struct	{platform, task_type, extra_tool_schemas, valid_actions, others{...}}

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

Image-dedup (grounding.* / understanding cohorts). These cohorts are single-image-per-row and many rows share the same screenshot, so to avoid re-embedding identical image bytes once per instruction they are stored folded: one row per unique screenshot (image embedded once), carrying an extra _folded column — a JSON string with the authoritative list of {messages, metadata} members for that screenshot. The row's top-level messages is the members concatenated for viewer convenience. use cohorts are not folded. Use lite.data.hf.download to consume this repo — it unfolds automatically back to one row per instruction; reading the parquet directly yields the folded form.

Layout

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

• platform ∈ {desktop, mobile, web}
• task_type ∈ {understanding, grounding.action, grounding.point, grounding.bbox, use} — used verbatim as the dir component
• HF config names are <platform>.<task_type> (e.g. mobile.grounding.action). The agent registry lookup key in code is <agent>@<platform>@<task_type> (e.g. qwen3_vl@mobile@grounding.action); only this user-facing token uses . between platform and task_type, because @ triggers a 403 on the dataset-viewer's signed image URLs.
• HF split names stay train / validation (the datasets library blacklists <>:/\|?* in split names; everything else is fine in config_name)
• 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
mobile	understanding	understanding	125,893	2,000
mobile	use	use	8,146	184

Local mirror & SFT export

For local workflows (SFT export, dedup, mixing across datasets), use lite.data.hf.download to mirror this repo back to the canonical local layout:

$CUA_LITE_DATASETS_ROOT/cua-lite/GUIOdyssey/
  images/<hash[:2]>/<hash>.<ext>                          # content-addressed image store
  <platform>/<task_type>/<split>[/<variant>].parquet      # rows reference images by relative path

Rows in the local parquet have images: list[str]; bytes are extracted to the image store. lite.train.export.export_sft consumes the local form directly with --image-root=$CUA_LITE_DATASETS_ROOT.

• Total unique images: 125,131
• Image store size: 92.16 GB

Notes

Source info coordinates are already normalized to [0, 1000] (per the upstream README) and are used directly — they are NOT device pixels.

License & citation

See original dataset (hflqf88888/GUIOdyssey).

See https://huggingface.co/datasets/hflqf88888/GUIOdyssey