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pretty_name: Pattern Over Pixels Screenshot-to-Code
license: other
task_categories:
- image-to-text
- text-generation
tags:
- screenshot-to-code
- multimodal
- html
- css
- visual-grounding
size_categories:
- n<1K
configs:
- config_name: default
default: true
data_files:
- split: train
path: "data/train-*.parquet"
---
# Pattern Over Pixels Screenshot-to-Code
This dataset contains controlled counterfactual screenshot-to-code examples built
from 30 real-world webpages from Design2Code. Each example preserves a repeated
UI pattern while introducing a single localized deviation, allowing researchers
to test whether multimodal models follow the pixels or simply restore the
dominant template.
## Contents
- 720 perturbed HTML instances
- 360 structural-card examples
- 360 text-style examples
- 2 screenshot conditions per example:
- `standard_image`
- `noise_image`
## Layout
```text
hf_release/
README.md
.gitattributes
data/
examples.jsonl
metadata/
dataset_info.json
validation_report.json
html/
cards/
texts/
images/
cards/
standard/
noise/
texts/
standard/
noise/
```
## Row Schema
Each line in `data/examples.jsonl` is one perturbed HTML instance with both
image conditions attached. Important fields include:
- `example_id`
- `source_id`
- `pattern_family`
- `pattern_name`
- `attribute_name`
- `position`
- `target_value`
- `bias_value`
- `html_path`
- `standard_image`
- `noise_image`
- `code_snippet_line`
- `code_snippet_context`
## Loading
Load the local release folder with:
```python
from datasets import load_dataset, Image
ds = load_dataset("json", data_files="data/examples.jsonl", split="train")
ds = ds.cast_column("standard_image", Image())
ds = ds.cast_column("noise_image", Image())
```
After pushing to the Hub with `scripts/push_hf_dataset.py`, load it with:
```python
from datasets import load_dataset
ds = load_dataset("AnonymousSubmissionASE/pixels_vs_code", split="train")
```
## Notes
- `standard_image` and `noise_image` are relative paths inside the dataset repo.
- The Hub push script materializes `html_content` and embeds image bytes into the
Parquet shards so the hosted dataset is self-contained.
- This release is organized for public hosting and loading, not for regeneration.
- The underlying webpages originate from real-world websites. Check original
website licenses and terms before downstream redistribution or commercial use.
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