--- 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 SeeClick-Web size_categories: - 1M Web element grounding and referring pairs derived from the SeeClick web corpus. ## Dataset at a glance | | | | :--- | :--- | | **Total samples** | 4,935,436 | | **Valid images** | 265,080 | | **Avg. samples / image** | 18.62 | | **Coordinate scale** | 0–1000 | | **Source dataset** | [SeeClick-Web](https://github.com/njucckevin/SeeClick) | ### Samples by task | Task | Count | | :--- | ---: | | ElemGnd | 2,467,718 | | ElemRef | 2,467,718 | ## Repository file structure | File | Description | | :--- | :--- | | `SeeClick-Web_ElemGnd_ElemRef_4935k.json` | The dataset: a JSON **list** of 4,935,436 sample objects (schema below). | | `SeeClick-Web_ElemGnd_ElemRef_4935k_sample.json` | A small preview slice of the same schema, for quick inspection without downloading everything. | | `SeeClick-Web_ElemGnd_ElemRef_4935k_images.zip` | All screenshots referenced by the `image` field, preserving the relative paths stored there. | | `SeeClick-Web_ElemGnd_ElemRef_4935k_info.json` | Full generation report — per-task counts, image statistics, invalid-element breakdown, and the exact processing config. | Unzip `SeeClick-Web_ElemGnd_ElemRef_4935k_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 `` placeholder marking where the screenshot is inserted; the **gpt** turn is the ground-truth answer. | | `elem_role` | Accessibility/DOM role of the target element (e.g. `text`, `link`, `button`), carried over from the SeeClick web annotations. | | `id` | Unique sample identifier, formatted `autogui___`. The `` 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. | | `url` | Source web page URL the screenshot was captured from. | > **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 **``** 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_SeeClick-Web_elemref_856936", "conversations": [ { "from": "human", "value": "\nPlease generate a brief description for the element at (263,735) (with point)" }, { "from": "gpt", "value": "Morgans" } ], "image": "SeeClick-Web/a7f6db97dcb3c2c876da06bb0dbac625.png", "unnormalized_box": [ 484, 788, 526, 800 ], "task_attr": "(263,735)", "elem_role": "text", "url": "https://www.countrybrewer.com.au/pages/Our-History.html" } ``` ## Usage ```python import json, os, zipfile from PIL import Image from huggingface_hub import hf_hub_download repo = "HongxinLi/UIPro-SeeClickWeb-Data-v1" samples = json.load(open(hf_hub_download(repo, "SeeClick-Web_ElemGnd_ElemRef_4935k.json", repo_type="dataset"))) images_zip = hf_hub_download(repo, "SeeClick-Web_ElemGnd_ElemRef_4935k_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, [SeeClick-Web](https://github.com/njucckevin/SeeClick). ## 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} } ```