cua-lite/GUIAct

---
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
data_files:
- split: train
path:
- "mobile/*/train*parquet"
- "mobile/*/train/*.parquet"
- "mobile/*/train/*/*.parquet"
- split: validation
path:
- "mobile/*/validation*parquet"
- "mobile/*/validation/*.parquet"
- "mobile/*/validation/*/*.parquet"
- config_name: web
data_files:
- split: train
path:
- "web/*/train*parquet"
- "web/*/train/*.parquet"
- "web/*/train/*/*.parquet"
- split: validation
path:
- "web/*/validation*parquet"
- "web/*/validation/*.parquet"
- "web/*/validation/*/*.parquet"
- config_name: mobile-trajectory
data_files:
- split: train
path:
- "mobile/trajectory/train*parquet"
- "mobile/trajectory/train/*.parquet"
- "mobile/trajectory/train/*/*.parquet"
- split: validation
path:
- "mobile/trajectory/validation*parquet"
- "mobile/trajectory/validation/*.parquet"
- "mobile/trajectory/validation/*/*.parquet"
- config_name: web-grounding-action
data_files:
- split: train
path:
- "web/grounding-action/train*parquet"
- "web/grounding-action/train/*.parquet"
- "web/grounding-action/train/*/*.parquet"
- split: validation
path:
- "web/grounding-action/validation*parquet"
- "web/grounding-action/validation/*.parquet"
- "web/grounding-action/validation/*/*.parquet"
- config_name: web-trajectory
data_files:
- split: train
path:
- "web/trajectory/train*parquet"
- "web/trajectory/train/*.parquet"
- "web/trajectory/train/*/*.parquet"
- split: validation
path:
- "web/trajectory/validation*parquet"
- "web/trajectory/validation/*.parquet"
- "web/trajectory/validation/*/*.parquet"
---

# cua-lite/GUIAct

cua-lite preprocessed version of GUIAct (yiye2023/GUIAct). Three variants: web_single (single-screenshot grounding:action, 66k+1.4k rows), web_multi (multi-step web episodes), and smartphone (Android episodes). Each variant carries the upstream train + test split in metadata.others.split; the test partition is promoted to our canonical ``validation`` via the upstream-split map.

## Origin

- [https://huggingface.co/datasets/yiye2023/GUIAct](https://huggingface.co/datasets/yiye2023/GUIAct)

## Load via `datasets`

```python
from datasets import load_dataset

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

# just one platform
ds = load_dataset("cua-lite/GUIAct", "mobile")

# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/GUIAct", "mobile-trajectory")
```

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 |
|---|---|---|---:|---:|
| mobile | trajectory | smartphone | 7,246 | 230 |
| web | grounding:action | web_single | 66,240 | 1,410 |
| web | trajectory | web_multi | 5,501 | 193 |

## 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`](https://github.com/cua-lite/cua-lite/tree/main/scripts/hf_upload)
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: **117,696**
- Store size: **11.98 GB**

## Notes

_(none)_

## License & citation

See original dataset (yiye2023/GUIAct)

See https://huggingface.co/datasets/yiye2023/GUIAct