| --- |
| pretty_name: EditJudge-Bench Evaluation Code |
| tags: |
| - benchmark |
| - evaluation |
| - vision-language-models |
| - image-editing |
| - vlm-as-a-judge |
| --- |
| |
| # EditJudge-Bench Evaluation Code |
|
|
| This repository contains lightweight, anonymous evaluation utilities for the |
| EditJudge-Bench dataset release. The code validates a local dataset snapshot, expands |
| edit-level rows into verification triplets, and computes the AUROC metrics used |
| to audit VLM-as-a-judge behaviour. |
|
|
| Dataset URL: `https://huggingface.co/datasets/EDAnonSubmission/benchmark` |
|
|
| ## Install |
|
|
| ```bash |
| python -m venv .venv |
| source .venv/bin/activate |
| pip install -r requirements.txt |
| ``` |
|
|
| ## Dataset Layout |
|
|
| The scripts expect a local dataset snapshot with: |
|
|
| ```text |
| benchmark.parquet |
| images/<sample_id>/before.jpg |
| images/<sample_id>/after.jpg |
| ``` |
|
|
| Each parquet row is one edit pair. A positive verification triplet is created |
| from `instruction_pos`; negative triplets are created from |
| `instruction_neg_list` and `instruction_neg_types`. |
|
|
| ## Validate the Dataset |
|
|
| ```bash |
| python scripts/validate_dataset.py \ |
| --dataset-root /path/to/benchmark |
| ``` |
|
|
| The validator checks row count, image coverage, negative-instruction alignment, |
| edit-type balance, and that image paths are portable and repo-relative. |
|
|
| ## Prediction Schema |
|
|
| Prediction files may be CSV, JSONL, JSON, or Parquet. The simplest schema is: |
|
|
| ```text |
| sample_id,example_type,negative_index,score |
| ``` |
|
|
| For positives, set `example_type=positive` and `negative_index=-1`. For negatives, |
| set `example_type=negative` and use the index in `instruction_neg_list`. |
|
|
| The scripts also accept `parquet_row_index` instead of `sample_id`, or an |
| expanded table that already contains `label`, `edit_type`, `negative_type`, and |
| `score`. |
|
|
| ## Compute Main Metrics |
|
|
| ```bash |
| python scripts/compute_benchpress_metrics.py \ |
| --dataset-root /path/to/benchmark \ |
| --predictions /path/to/predictions.parquet \ |
| --out-dir outputs/judge_name |
| ``` |
|
|
| Outputs: |
|
|
| - `overall_metrics.csv`: global AUROC and macro edit-type AUROC. |
| - `per_edit_type_auc.csv`: AUROC for each edit type. |
|
|
| ## Negative-Type Breakdown |
|
|
| ```bash |
| python scripts/compute_negative_type_breakdown.py \ |
| --dataset-root /path/to/benchmark \ |
| --predictions /path/to/predictions.parquet \ |
| --out-dir outputs/judge_name |
| ``` |
|
|
| Outputs: |
|
|
| - `per_negative_type_auc.csv` |
| - `semantic_vs_noedit_summary.csv` |
|
|
| ## Ground-Truth Salience Tables |
|
|
| ```bash |
| python scripts/make_salience_tables.py \ |
| --dataset-root /path/to/benchmark \ |
| --predictions /path/to/predictions.parquet \ |
| --out-dir outputs/judge_name \ |
| --bins 5 |
| ``` |
|
|
| This conditions no-edit rejection AUROC on saved Blender parameters such as |
| shape delta, articulation delta, scale delta, movement distance, rotation angle, |
| lighting magnitude, and camera changes. |
|
|
| ## Notes |
|
|
| This code release evaluates judge scores; it does not run VLM inference. The |
| paper's model-specific prompts and inference wrappers are implementation details |
| around producing the prediction files consumed here. |
|
|
