README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - image-classification
  - visual-question-answering
tags:
  - image-editing
  - benchmark
  - synthetic-data
  - vlm-as-a-judge
  - croissant
pretty_name: EditJudge-Bench
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: benchmark.parquet

EditJudge-Bench

EditJudge-Bench is a synthetic benchmark for auditing vision-language models used as automated judges for image-edit verification. Each row contains a source image, an edited image, a factual edit instruction, counterfactual instructions, and ground-truth scene parameters produced by a controlled Blender/Infinigen generation pipeline.

This repository is an anonymous review release for a NeurIPS Evaluations and Datasets submission.

Dataset Contents

• 1,500 edit pairs.
• 3,000 JPEG images, stored as paired before and after files.
• 10 stored edit categories, with 150 examples each.
• The paper reports 9 categories by combining object_material and material_slot into a single material category.
• One row per edit pair, not one row per positive/negative triplet.

Edit-type counts:

• articulation: 150
• camera: 150
• lighting: 150
• material_slot: 150
• movement: 150
• object_material: 150
• removal: 150
• rotation: 150
• scale: 150
• shape: 150

Files

benchmark.parquet
croissant.json
images/<sample_id>/before.jpg
images/<sample_id>/after.jpg

The before and after columns in benchmark.parquet are repository-relative paths. Resolve them relative to the downloaded dataset snapshot.

Row Format

Important columns include:

• sample_id: stable row identifier.
• edit_type: edit category.
• before, after: repository-relative image paths.
• instruction_pos: factual instruction that matches the image pair.
• instruction_neg_list: list of counterfactual instructions for the same image pair.
• instruction_neg_types: counterfactual type for each negative instruction.
• metadata and meta.* / params.*: ground-truth procedural parameters saved from the Blender generation process.

To evaluate a judge, score the positive triplet (before, after, instruction_pos) and compare it against negative triplets (before, after, instruction_neg_list[i]).

Intended Use

EditJudge-Bench is intended for diagnostic evaluation of VLM-based editing judges: whether a model can verify that an image edit was correctly executed, reject counterfactual edit instructions, and expose failures as a function of known ground-truth scene parameters.

Out-of-Scope Use

EditJudge-Bench is not intended as a general-purpose training set for image generation, a benchmark for human ability, or a safety-critical evaluation resource. The images are synthetic indoor scenes and should not be treated as representative of all real-world editing scenarios.

Responsible AI Notes

The data are rendered synthetic indoor scenes and are not scraped from people, social media, surveillance footage, or private sources. The benchmark may still reflect the procedural biases of the scene generator, including object categories, room layouts, material distributions, and camera placement.

Known limitations include the synthetic-to-real gap, JPEG compression artifacts, coverage of a finite set of edit types, and the fact that the dataset audits judges rather than image-editing models directly.

License

This dataset is released under CC-BY-NC-4.0.

Croissant

Machine-readable metadata are provided in croissant.json, including Responsible AI metadata for dataset review.

Dataset URL used in metadata: https://huggingface.co/datasets/EDAnonSubmission/benchmark.