README.md

---
license: bsl-1.0
pretty_name: Widget2Code Data
task_categories:
  - image-to-text
tags:
  - screenshot-to-code
  - react
  - jsx
  - multimodal
---

# Widget2Code Data

Widget screenshots paired with the evaluation evidence that depends only on
them, plus self-contained image-code examples.

## Directory layout

```text
train/       # 1,822 widget screenshots
test/        # 1,000 widget screenshots
sft/         # 1,816 complete examples
sft-v3/       # raw Gemini 3.1 Pro generations, split into train/ and test/
sft-v3-fixed/ # policy-repaired, fully renderable versions of sft-v3
agentic/     # 232 teacher/student correction trajectories and their renders

verify_draft_qwen35_4b_20260827/        # full-SFT 4B outputs on train and test
verify_draft_qwen35_9b_lora_20260827/   # merged LoRA-SFT 9B outputs on train and test
verify_draft_qwen35_27b_20260827/       # merged LoRA-SFT 27B outputs on train and test

sft-v3/<train|test>/image_0004/
sft-v3-fixed/<train|test>/image_0004/
├── image.png        # the widget
├── metadata.json    # labels + precomputed evaluation intermediates
├── ocr.txt          # OCR evidence, as fed to a prompt
├── palette.txt      # palette evidence, as fed to a prompt
├── code.jsx         # JSX paired with the target image
├── rendered.png     # sft-v3*: generated code render; absent on raw render failures
├── dims.txt         # exact target width and height
└── evaluation/
    ├── evaluation.json        # metrics for a successful render
    ├── ocr.json               # OCR results for target and render
    ├── evaluation_black.json  # raw render failures only
    └── evaluation_white.json  # raw render failures only
```

The three `verify_draft_*` directories preserve one stochastic generation per
benchmark sample. Each split has a `summary.json`; each sample directory has the
raw response, extracted `widget.jsx`, `meta.json`, and `rendered.png` when render
succeeded. The model sizes used the same inference contract, but their training
recipes differ, so these outputs are validation artifacts rather than a pure
parameter-scaling ablation.

`sft/` is a separate split, not a view of `train/`: its `image.png` is the
render of `code.jsx`, not the original screenshot of the same id, and its OCR
and palette describe that render.

`sft-v3/` and `sft-v3-fixed/` use the corresponding original `train/` or
`test/` screenshot as `image.png`; their OCR and palette files describe that
target. `rendered.png` is the output of `code.jsx`. The train split contains
1,822 samples (1,706 raw renders and 1,822 fixed renders). The test split
contains 1,000 samples (936 raw renders and 1,000 fixed renders). Raw render
failures are retained as code examples with the error recorded in
`metadata.json`, and no mismatched render is substituted.

All four `sft-v3{,-fixed}/<train|test>` splits include prediction-side
evaluation caches produced by `widget2code-bench-exp` 1.0.0. A successful
render has `evaluation/evaluation.json` and `evaluation/ocr.json`. A raw sample
without a render instead has paired black/white fallback evaluations, so every
sample is covered without substituting the fixed render. Each split also has
`evaluation.xlsx` and `.eval_v1.0.0/metrics/` aggregate summaries.

The train code was generated with the archived v3 prompt. The test code was
generated with the v3.1 prompt in `core/generation/prompts/widget_simple.md`;
its prompt version and SHA-256 are recorded in each test `metadata.json`.

## metadata.json

```jsonc
{
  "id": "image_0004", "split": "train",
  "sha256": "…",            // of image.png; a cache is only valid for its bytes
  "size": [976, 668],
  "category": "calendar",   // train/sft: one of 16; test: null (never labelled)
  "has_chart": null,        // test: true/false; train/sft: null (never labelled)
  "eval": {
    "layout":     { "margin": [...], "mask_empty": false, "bbox_ar": ..., "area_ratio": ..., "n_comp": ... },
    "legibility": { "text": "...", "ocr": [[bbox, text, confidence], ...], "contrast": ..., "contrast_local": ... },
    "style":      { "hue_hist": [36], "sat_hist": [30], "polarity": [sign, strength] },
    "fill":       { "black": {...}, "white": {...} }
  }
}
```

`eval` holds the half of a benchmark score that depends on the ground truth
alone, so an evaluation run reads it instead of recomputing it for every model
it scores. Only SSIM and LPIPS against a prediction genuinely need both images.
`fill` is the score of the ground truth against an all-black and an all-white
image, used when a prediction is missing.

Values are full-precision floats: `float(repr(x)) == x`, so the text form
round-trips exactly. `null` means never labelled, not "known to be absent".

## Changes from the previous layout

`train_cls/` and `test_cls/` are gone. They held byte-identical copies of
`train/` and `test/` — 613 MB — to express a 16-way label that is now the
`category` field. `charts/` is gone for the same reason: it was 30 test images
already present in `test/`, and `has_chart` now covers all 1,000.

Files are raw and per sample; training code assembles records after download.