| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-to-text |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - gui |
| - gui-agent |
| - ui-understanding |
| - screenshot |
| - visual-grounding |
| pretty_name: UIPro WebUI |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # UIPro-WebUI-Data-v1 |
|
|
| Part of the **UIPro** GUI-agent training suite (ICCV 2025). This repository packages the |
| **WebUI** source into the unified UIPro instruction-tuning format, with coordinates |
| normalized to a **[0, 1000]** grid. |
|
|
| > Web-page screenshots with icon grounding/referring, element classification, intent grounding and widget listing. |
|
|
| ## Dataset at a glance |
|
|
| | | | |
| | :--- | :--- | |
| | **Total samples** | 488,499 | |
| | **Valid images** | 327,280 | |
| | **Avg. samples / image** | 1.49 | |
| | **Coordinate scale** | 0–1000 | |
| | **Source dataset** | [WebUI](https://huggingface.co/datasets/biglab/webui-all) | |
|
|
| ### Samples by task |
|
|
| | Task | Count | |
| | :--- | ---: | |
| | WidgetList | 160,266 | |
| | IconRef | 116,403 | |
| | IntentGnd | 110,800 | |
| | IconGnd | 59,561 | |
| | ElemClass | 41,469 | |
|
|
| ## Repository file structure |
|
|
| | File | Description | |
| | :--- | :--- | |
| | `WebUI_scale1000_488k.json` | The dataset: a JSON **list** of 488,499 sample objects (schema below). | |
| | `WebUI_scale1000_488k_sample.json` | A small preview slice of the same schema, for quick inspection without downloading everything. | |
| | `WebUI_scale1000_488k_images.zip` | All screenshots referenced by the `image` field, preserving the relative paths stored there. | |
| | `WebUI_scale1000_488k_info.json` | Full generation report — per-task counts, image statistics, invalid-element breakdown, and the exact processing config. | |
|
|
| Unzip `WebUI_scale1000_488k_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"|"gpt", "value": ...}`. The **human** turn holds the instruction/question and contains the `<image>` placeholder marking where the screenshot is inserted; the **gpt** turn is the ground-truth answer. | |
| | `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. | |
| | `wxh` | Original screenshot size as the string `"<width>x<height>"` in pixels — use it to convert between the normalized 0–1000 coordinates and raw pixels. | |
| |
| > **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 |
|
|
| ```json |
| { |
| "id": "autogui_webui_widgetlist_107976", |
| "conversations": [ |
| { |
| "from": "human", |
| "value": "<image>\nPlease list all the elements on the UI screen." |
| }, |
| { |
| "from": "gpt", |
| "value": "0 StaticText '3个月考上北大法硕，她说普通人成功很简单' (26,14,708,64)\n1 link '' (731,14,972,90)\n2 StaticText '时间：2022-07-20 18:11' (26,71,405,88)\n3 StaticText '老鼠吓退大猫抢吃猫粮 猫连连后退拱手相让' (26,121,692,171)\n4 link '' (731,121,972,197)\n5 StaticText '时间：2022-07-20 17:32' (26,178,405,194)\n6 StaticText '普通人镜头下的真生活' (26,227,462,251)\n7 link '' (731,227,972,303)\n8 StaticText '时间：2022-07-20 16:15' (26,284,405,301)\n9 StaticText '加载更多' (413,333,587,355)\n10 emphasis '' (26,416,33,440)\n11 StaticText '更多' (887,418,959,437)\n12 StaticText '吴亦凡打卡的面馆被整改' (26,461,505,485)\n13 link '' (731,461,972,537)\n14 StaticText '时间：2022-07-20 18:34' (26,518,405,534)\n15 StaticText '广州人才入户年龄放宽5年 本科40岁硕士45岁博士50岁' (26,568,690,617)\n16 link '' (731,568,972,643)\n17 StaticText '时间：2022-07-20 17:58' (26,624,405,641)\n18 StaticText '兵马俑手指在美被折断盗走' (26,674,549,698)\n19 link '' (731,674,972,750)\n20 StaticText '时间：2022-07-20 17:03' (26,731,405,748)\n21 StaticText '凤凰周刊：如果不靠高考，你该如何体面地出人头..' (26,781,679,831)\n22 link '' (731,781,972,857)\n23 StaticText '时间：2022-07-20 17:03' (26,838,405,854)\n24 StaticText '王中磊夫妇为儿子庆生 威廉弟弟长高了许多' (26,887,692,937)\n25 link '' (731,887,972,963)\n26 StaticText '时间：2022-07-20 16:16' (26,944,405,961)" |
| } |
| ], |
| "unnormalized_box": [ |
| [ |
| 30, |
| 36, |
| 828, |
| 162 |
| ], |
| [ |
| 855, |
| 36, |
| 1137, |
| 228 |
| ], |
| [ |
| 30, |
| 180, |
| 474, |
| 22 |
| ... (truncated) |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| import json, os, zipfile |
| from PIL import Image |
| from huggingface_hub import hf_hub_download |
| |
| repo = "HongxinLi/UIPro-WebUI-Data-v1" |
| samples = json.load(open(hf_hub_download(repo, "WebUI_scale1000_488k.json", repo_type="dataset"))) |
| |
| images_zip = hf_hub_download(repo, "WebUI_scale1000_488k_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, [WebUI](https://huggingface.co/datasets/biglab/webui-all). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
