---
license: other
tags:
- cua-lite
- gui
- sft
task_categories:
- image-text-to-text
configs:
- config_name: default
  data_files:
  - split: train
    path:
    - "*/*/train*parquet"
    - "*/*/train/*.parquet"
    - "*/*/train/*/*.parquet"
  - split: validation
    path:
    - "*/*/validation*parquet"
    - "*/*/validation/*.parquet"
    - "*/*/validation/*/*.parquet"
- config_name: mobile.navigation
  data_files:
  - split: train
    path:
    - "mobile/navigation/train*parquet"
    - "mobile/navigation/train/*.parquet"
    - "mobile/navigation/train/*/*.parquet"
  - split: validation
    path:
    - "mobile/navigation/validation*parquet"
    - "mobile/navigation/validation/*.parquet"
    - "mobile/navigation/validation/*/*.parquet"
- config_name: mobile.understanding
  data_files:
  - split: train
    path:
    - "mobile/understanding/train*parquet"
    - "mobile/understanding/train/*.parquet"
    - "mobile/understanding/train/*/*.parquet"
  - split: validation
    path:
    - "mobile/understanding/validation*parquet"
    - "mobile/understanding/validation/*.parquet"
    - "mobile/understanding/validation/*/*.parquet"
---

# 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](https://huggingface.co/datasets/hflqf88888/GUIOdyssey)

## Load via `datasets`

```python
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.navigation")
```

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_tools, 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. `navigation`
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, navigation} — 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 | navigation | navigation | 8,146 | 184 |
| mobile | understanding | understanding | 125,893 | 2,000 |

## 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.utils.data.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