---
pretty_name: Scry Design Diff Eval
license: cc-by-4.0
task_categories:
- image-to-text
- object-detection
tags:
- ui
- mobile
- visual-regression
- benchmark
- vlm
- screenshots
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# Scry Design Diff Eval

**Measuring VLMs as Mobile UI Regression Reviewers**

Scry Design Diff Eval is a benchmark built to evaluate vision-language models on
mobile UI diff review. Each example pairs a reference mobile screenshot
(`image_a`) with a generated implementation screenshot (`image_b`) and carries
human-drawn selection boxes plus explicit defect tags. A model must return a
structured list of UI defects — tags and normalized boxes — not a prose
description.

📄 **Paper**: https://blog.scrymore.com/

## Dataset composition

| Quantity | Count |
|---|---:|
| Eval pairs | 311 |
| Visual-diff pairs | 234 |
| No-tagged controls | 77 |
| Counted tagged issues | 557 |

Issue-density buckets (the `split` column):

| Bucket | Definition | Pairs |
|---|---|---:|
| `single_issue` | 1 tagged issue | 107 |
| `multi_issue` | 2-3 tagged issues | 80 |
| `dense_issue` | 4 or more tagged issues | 47 |
| `no_diff` | 0 scored tagged issues | 77 |

The most common defect tags are `Icon/Nav`, `Color/Background`,
`Spacing/Layout`, `Shape/Size`, `Missing Content`, and `Typography`.

## Schema

| Column | Type | Description |
|---|---|---|
| `id` | string | Pair id, e.g. `amazon-shopping__801` |
| `app_name` | string | Source app and capture batch |
| `app_slug` | string | Normalized app identifier |
| `screen_index` | int | Screen number within the app capture |
| `split` | string | Issue-density bucket (see above) |
| `task_type` | string | `visual_diff` or `no_diff` |
| `n_issues` | int | Number of scored tagged issues |
| `issue_labels` | list[string] | Union of defect tags on this pair |
| `ground_truth_issues` | string | JSON array of issues: `{issue_id, labels, box_a, box_b, note?, created_at}` with boxes normalized to `{x, y, w, h}` in [0, 1] |
| `image_a` | image | Reference screenshot |
| `image_b` | image | Generated implementation screenshot |

Parse `ground_truth_issues` with `json.loads`. A box may be present on side A,
side B, or both.

## Task

Given `image_a` and `image_b`, return:

```json
{
  "issues": [
    {
      "labels": ["Icon/Nav"],
      "note": "The bottom navigation icon differs from the reference.",
      "box_a": {"x": 0.10, "y": 0.90, "w": 0.12, "h": 0.07},
      "box_b": {"x": 0.10, "y": 0.90, "w": 0.12, "h": 0.07},
      "confidence": 0.80
    }
  ]
}
```

## Scoring

The primary metric is **known-issue recall**. A model issue matches a human
issue when they share at least one explicit defect tag AND the model box
overlaps the human box on the same image side with IoU >= 0.10. Matching is
one-to-one. The judge is deterministic — no LLM judging.

The protocol is recall-first because human annotations are known positives, not
exhaustive negatives: extra model findings may be valid and are reported
diagnostically (precision, no-tagged flag rate) rather than reducing the
primary score.

## Baseline results (full 311-pair set)

| Model | Known-Issue Recall | Diagnostic Precision | Issue F1 |
|---|---:|---:|---:|
| Kimi K2.7 Code + Together recovery | 38.2% | 15.9% | 22.5% |
| Gemini 3.5 Flash | 37.5% | 20.2% | 26.3% |
| Codex GPT-5.5 xhigh | 37.3% | 13.6% | 19.9% |
| MiniMax M3 | 21.9% | 11.5% | 15.0% |
| Gemma 4 26B A4B | 20.3% | 12.4% | 15.4% |
| Gemma 4 31B | 17.8% | 13.3% | 15.2% |

See the paper for pilot results, density and category breakdowns, and static
controls.

## Caveats

- **No-tagged controls are a proxy, not a guarantee**: pairs with zero scored
  tagged issues are used as controls but were not exhaustively audited as
  defect-free.
- **Annotations are known positives**: a model can find a real defect the
  annotators did not tag. Treat precision and no-diff specificity as
  operational diagnostics.
- **Screenshots**: reference images are captures of real mobile apps, included
  for research and evaluation purposes; all app content remains the property
  of its respective owners. The annotations (boxes, tags, metadata) are
  released under CC BY 4.0.

## Citation

```bibtex
@misc{scrydesigndiffeval2026,
  title={Scry Design Diff Eval: Measuring VLMs as Mobile UI Regression Reviewers},
  author={Pinnock, Ejiro},
  year={2026},
  url={https://blog.scrymore.com/}
}
```