UIPro-WidgetCaptioning-Data-v1

Part of the UIPro GUI-agent training suite (ICCV 2025). This repository packages the Widget Captioning source into the unified UIPro instruction-tuning format, with coordinates normalized to a [0, 1000] grid.

Widget-caption grounding on RICO mobile screenshots (element + intent grounding).

Dataset at a glance

Total samples	109,359
Valid images	14,878
Avg. samples / image	7.35
Coordinate scale	0–1000
Source dataset	Widget Captioning

Samples by task

Task	Count
IntentGnd	62,954
ElemGnd	46,405

Repository file structure

File	Description
WidgetCaptioning_109k.json	The dataset: a JSON list of 109,359 sample objects (schema below).
WidgetCaptioning_109k_sample.json	A small preview slice of the same schema, for quick inspection without downloading everything.
WidgetCaptioning_109k_images.zip	All screenshots referenced by the image field, preserving the relative paths stored there.
WidgetCaptioning_109k_info.json	Full generation report — per-task counts, image statistics, invalid-element breakdown, and the exact processing config.

Unzip WidgetCaptioning_109k_images.zip and each sample's image path resolves relative to the extraction root.

Sample schema — every field explained

Each element of the main JSON list is one training sample. This dataset's samples use the following fields:

Field	Meaning
conversations	The vision-language dialogue: a list of turns, each `{"from": "human"
id	Unique sample identifier, formatted autogui_<dataset>_<task>_<n>. The <task> segment (e.g. intentgnd, textloc, ocr, elemgnd, elemref) tells you which task the sample belongs to.
image	Path to the screenshot inside _images.zip, relative to the archive root. Load the image by joining this path with your extraction directory.
task_attr	The task's target attribute in plain form — for grounding tasks the referred element's text/instruction; for OCR/referring tasks the queried coordinate string. Useful for filtering or building custom prompts without parsing the conversation.
unnormalized_box	Ground-truth bounding box in original image pixels, as [x1, y1, x2, y2] (top-left, bottom-right). Present when a box is available. Note: the answer in the gpt turn is normalized to 0–1000, while this field is the raw-pixel box — divide by width/height and multiply by 1000 to reconcile them.

Note: Fields tied to a bounding box (e.g. unnormalized_box) are only present on samples that have a box; point-only answers (e.g. some intent-grounding samples) may omit them.

The conversations field in detail

conversations is a list of turns that a vision-language model consumes directly:

• Each turn is {"from": "...", "value": "..."}.
• from is either human (the prompt) or gpt (the ground-truth response).
• The token <image> inside a human turn marks where the screenshot is spliced into the prompt — replace it with the actual image when tokenizing.

Coordinate system

• Answers are normalized to the 0–1000 range relative to image width/height.
• A point answer is formatted (x,y); a bounding box answer is (x1,y1,x2,y2).
• Prompts ending in (with point) expect a point; (with bbox) expect a box.
• unnormalized_box, when present, is the same box in raw pixels — combine it with wxh (or the image's true size) to convert between pixels and the normalized grid.

Example

{
  "id": "autogui_WidgetCaptioning_elemgnd_14602",
  "conversations": [
    {
      "from": "human",
      "value": "<image>\nWhere is the \"clear text\" element?"
    },
    {
      "from": "gpt",
      "value": "(947,139)"
    }
  ],
  "image": "rico/16883.jpg",
  "unnormalized_box": [
    482,
    105,
    540,
    163
  ],
  "task_attr": "clear text"
}

Usage

import json, os, zipfile
from PIL import Image
from huggingface_hub import hf_hub_download

repo = "HongxinLi/UIPro-WidgetCaptioning-Data-v1"
samples = json.load(open(hf_hub_download(repo, "WidgetCaptioning_109k.json", repo_type="dataset")))

images_zip = hf_hub_download(repo, "WidgetCaptioning_109k_images.zip", repo_type="dataset")
with zipfile.ZipFile(images_zip) as zf:
    zf.extractall("images/")

s = samples[0]
print(s["conversations"])
img = Image.open(os.path.join("images", s["image"]))   # screenshot for this sample
print("image size:", img.size)

About UIPro

UIPro is a generalist GUI agent trained on 20.6M understanding tasks across 13 task types, followed by agent continued fine-tuning. See the project repository for the full data pipeline, training recipes and evaluation scripts: https://github.com/ZJULiHongxin/UIPro

License

Released under CC BY-NC 4.0 (non-commercial research use). The underlying screenshots and annotations remain subject to the terms of their original source, Widget Captioning.

Citation

@inproceedings{uipro2025,
  title     = {UIPro: A Generalist GUI Agent},
  author    = {Li, Hongxin and others},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2025}
}