cua-lite/AgentNet

---
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: desktop-trajectory
data_files:
- split: train
path:
- "desktop/trajectory/train*parquet"
- "desktop/trajectory/train/*.parquet"
- "desktop/trajectory/train/*/*.parquet"
- split: validation
path:
- "desktop/trajectory/validation*parquet"
- "desktop/trajectory/validation/*.parquet"
- "desktop/trajectory/validation/*/*.parquet"
---

# cua-lite/AgentNet

cua-lite preprocessed version of AgentNet (xlangai/AgentNet). Desktop trajectory data collected via the OpenCUA project, covering Ubuntu (ubuntu variant) and optionally Windows/macOS (win_mac variant) workflows.

## Origin

- [https://huggingface.co/datasets/xlangai/AgentNet](https://huggingface.co/datasets/xlangai/AgentNet)

## Load via `datasets`

```python
from datasets import load_dataset

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

# just one (platform, task_type) cohort
ds = load_dataset("cua-lite/AgentNet", "desktop-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 |
|---|---|---|---:|---:|
| desktop | trajectory | ubuntu | 4,900 | 92 |

## 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: **82,171**
- Store size: **73.74 GB**

## Notes

_(none)_

## License & citation

See original dataset (xlangai/AgentNet)

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