Add dataset card (README) with schema and field documentation

README.md

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+---
+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
+- ocr
+pretty_name: UIPro GUIEnv
+size_categories:
+- 100K<n<1M
+---
+
+# UIPro-GUIEnv-Data-v1
+
+Part of the **UIPro** GUI-agent training suite (ICCV 2025). This repository packages the
+**GUICourse / GUIEnv** source into the unified UIPro instruction-tuning format, with coordinates
+normalized to a **[0, 1000]** grid.
+
+> Text localization and OCR samples from the GUICourse GUIEnv subset.
+
+## Dataset at a glance
+
+| | |
+| :--- | :--- |
+| **Total samples** | 557,808 |
+| **Valid images** | 557,808 |
+| **Avg. samples / image** | 1.0 |
+| **Coordinate scale** | 0–1000 |
+| **Source dataset** | [GUICourse / GUIEnv](https://github.com/yiye3/GUICourse) |
+
+### Samples by task
+
+| Task | Count |
+| :--- | ---: |
+| OCR | 286,828 |
+| TextLoc | 270,980 |
+
+## Repository file structure
+
+| File | Description |
+| :--- | :--- |
+| `guienv-train-s1000-557808.json` | The dataset: a JSON **list** of 557,808 sample objects (schema below). |
+| `guienv-train-s1000-557808_sample.json` | A small preview slice of the same schema, for quick inspection without downloading everything. |
+| `guienv-train-s1000-557808_images.zip` | All screenshots referenced by the `image` field, preserving the relative paths stored there. |
+| `guienv-train-s1000-557808_info.json` | Full generation report — per-task counts, image statistics, invalid-element breakdown, and the exact processing config. |
+
+Unzip `guienv-train-s1000-557808_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_GUIEnv_ocr_203372",
+  "conversations": [
+    {
+      "from": "human",
+      "value": "<image>\nWhat is the text written on this UI element at (826,856) (with point)?"
+    },
+    {
+      "from": "gpt",
+      "value": "Notification about November 23rd schelude."
+    }
+  ],
+  "image": "GUICourse/GUIEnv/imgs/C4web50k-2_184290590-split-0.png",
+  "task_attr": "(826,856)",
+  "wxh": "954x1000",
+  "unnormalized_box": [
+    671,
+    849,
+    905,
+    863
+  ]
+}
+```
+
+## Usage
+
+```python
+import json, os, zipfile
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+repo = "HongxinLi/UIPro-GUIEnv-Data-v1"
+samples = json.load(open(hf_hub_download(repo, "guienv-train-s1000-557808.json", repo_type="dataset")))
+
+images_zip = hf_hub_download(repo, "guienv-train-s1000-557808_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, [GUICourse / GUIEnv](https://github.com/yiye3/GUICourse).
+
+## 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}
+}
+```