Djanghao/Widget2Code-Data-V4

Widget2Code Data V4

Widget2Code Data V4 contains UI screenshots for training and evaluation, plus renderable JSX reference examples. Prompt-ready image information and reusable ground-truth evaluation features are stored directly in each screenshot's metadata.json.

Dataset layout

directory	samples	contents
train/	1,822	training screenshots and canonical metadata
test/	1,000	evaluation screenshots and canonical metadata
sft-v4/train/	1,822	JSX references and their renders, linked to train/
sft-v4/test/	1,000	JSX references and their renders, linked to test/

The canonical screenshot pools use this two-file layout:

train/image_0004/
test/image_0001/
├── image.png
└── metadata.json

The SFT reference pools do not duplicate the target screenshots:

sft-v4/train/image_0004/
sft-v4/test/image_0001/
├── code.jsx
├── rendered.png
└── metadata.json

An SFT sample's metadata.json contains source_split: train or source_split: test. Combine that split with the same sample ID to locate its target screenshot. rendered.png is the output of code.jsx; it is not the target image. The test references are generated examples, not ground-truth evaluation labels, and normal evaluation reads only test/.

Screenshot metadata

Each train/ and test/ metadata file has this shape:

{
  "id": "image_0004",
  "split": "train",
  "sha256": "...",          // SHA-256 of image.png
  "category": "tools",     // null when not labelled
  "has_chart": null,        // null when not labelled
  "eval": {
    "layout": { "margin": [], "mask_empty": false, "bbox_ar": 0.0, "area_ratio": 0.0, "n_comp": 0 },
    "legibility": { "text": "...", "contrast": 0.0, "contrast_local": 0.0 },
    "style": { "hue_hist": [], "sat_hist": [], "polarity": [] },
    "fill": { "black": {}, "white": {} }
  },
  "side_info": {
    "dims": [976, 668],
    "ocr": "- `\"Pesquisar\"` at (16.5%, 10.0%) of widget, font-height ≈ 7.2% of widget height",
    "palette": "Target widget palette (top-4, after AA-fringe consolidation):\n  #353A38    64.63%\n  ..."
  }
}

side_info is the complete prompt-ready representation derived from image.png: exact [width, height], formatted OCR output, and the formatted dominant palette. Consumers should read these values directly; there are no dims.txt, ocr.txt, or palette.txt sidecars.

eval caches the features that depend only on the ground-truth screenshot: geometry/layout, OCR text and contrast, color/style, and missing-prediction fill scores. A benchmark run can reuse these values for every prediction instead of recomputing the ground-truth side. Pairwise metrics such as SSIM and LPIPS still require the prediction image.

SFT metadata

Each sft-v4 metadata file identifies the canonical target and records generation provenance:

{
  "source_split": "train",
  "generation": {
    "model": "gemini-3.1-pro-preview",
    "variant": "policy-repaired",
    "code_sha256": "...",
    "render_ok": true,
    "rendered_sha256": "..."
  }
}

Additional prompt, renderer, and validation fields are retained where they were recorded. All 2,822 references have a successful render.

Rebuilding prompt side information

The canonical metadata was prepared with the Widget2Code benchmark 1.2.0 CPU container. On the 96-vCPU reference host, 64 workers was the measured best setting:

for split in train test; do
  docker run --rm --user "$(id -u):$(id -g)" -e HOME=/tmp \
    -v "$PWD/$split:/data" \
    houstonzhang/w2c-bench:1.2.0 widget2code-sideinfo \
    /data --out /data --workers 64 --force
done

CPU output is canonical. GPU OCR follows a different numeric path and is not guaranteed to produce byte-identical OCR text.

Download

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Djanghao/Widget2Code-Data-V4",
    repo_type="dataset",
    local_dir="Widget2Code-Data-V4",
)

Files are stored raw and per sample. Training code can assemble its preferred record format after download.