Hugging Face's logo

Datasets:
MoeNew
/
LocateEdit-Bench

Tasks:
Image Segmentation
Image-to-Image
Modalities:
Image
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
image-editing
instruction-based-editing
forgery-localization
manipulation-localization
benchmark
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
LocateEdit-Bench / README.md
MoeNew's picture
MoeNew
Update BibTeX author format
602e040 verified
preview code
|
Raw History Blame Contribute Delete
10.1 kB
metadata 
pretty_name: LocateEdit-Bench
license: cc-by-nc-4.0
language:
  - en
task_categories:
  - image-segmentation
  - image-to-image
tags:
  - image-editing
  - instruction-based-editing
  - forgery-localization
  - manipulation-localization
  - benchmark
configs:
  - config_name: all
    data_files:
      - split: train
        path: train/*/*.parquet
      - split: validation
        path: val/*/*.parquet
  - config_name: BAGEL
    data_files:
      - split: train
        path: train/BAGEL/*.parquet
      - split: validation
        path: val/BAGEL/*.parquet
  - config_name: Flux-Kontext
    data_files:
      - split: train
        path: train/Flux-Kontext/*.parquet
      - split: validation
        path: val/Flux-Kontext/*.parquet
  - config_name: Qwen-Image-Edit
    data_files:
      - split: train
        path: train/Qwen-Image-Edit/*.parquet
      - split: validation
        path: val/Qwen-Image-Edit/*.parquet
  - config_name: Step1X-Edit
    data_files:
      - split: train
        path: train/Step1X-Edit/*.parquet
      - split: validation
        path: val/Step1X-Edit/*.parquet
dataset_info:
  - config_name: all
    features: &ref_0
      - name: img_name
        dtype: string
      - name: model
        dtype: string
      - name: split
        dtype: string
      - name: row_index
        dtype: int32
      - name: edit_type
        dtype: string
      - name: edit_subtype
        dtype: string
      - name: edit_prompt
        dtype: string
      - name: sam_prompt
        dtype: string
      - name: boxes
        sequence:
          sequence: float64
      - name: scores
        sequence: float64
      - name: image
        dtype: image
      - name: edited_image
        dtype: image
      - name: mask
        dtype: image
  - config_name: BAGEL
    features: *ref_0
  - config_name: Flux-Kontext
    features: *ref_0
  - config_name: Qwen-Image-Edit
    features: *ref_0
  - config_name: Step1X-Edit
    features: *ref_0

LocateEdit-Bench

A Benchmark for Instruction-Based Editing Localization

LocateEdit-Bench is a large-scale benchmark for localizing the regions modified by instruction-based image editors. Unlike inpainting-based manipulations, where the edited area is given by an explicit mask, instruction-based editors (e.g., MMDiT-based models) produce semantically coherent and visually seamless edits without any predefined region. LocateEdit-Bench provides 231K edited images from four state-of-the-art editors, each paired with its original image, the editing instruction, and a pixel-level ground-truth mask.

Paper: arXiv:2602.05577

Highlights

  • 231,092 edited images from four instruction-based editors: Qwen-Image-Edit, Flux-Kontext, Step1X-Edit, and BAGEL.
  • Three object-level edit types: object addition, object swap, and attribute modification — edits with a well-defined, object-localized region.
  • High-quality masks: target object identified by ChatGPT, segmented by SAM3, filtered at confidence > 0.5.
  • 5,504 distinct object types; source images drawn from LAION-5B and OpenImages V6 (via OmniEdit).
  • Dedicated train/validation splits per editor: the validation split is the paper's 24K test set (6,000 images per editor, stratified by edit type).

Dataset structure 

LocateEdit-Bench/
├── manifest.json      # per-shard split, row-index range, relative path
├── splits.json        # {model: {train: [files], val: [files]}}
├── stats.json         # rows / edit-type counts / sizes per model
├── summary.json       # global totals
├── train/
│   ├── BAGEL/BAGEL_train_00001.parquet ...
│   ├── Flux-Kontext/Flux-Kontext_train_00001.parquet ...
│   ├── Qwen-Image-Edit/Qwen-Image-Edit_train_00001.parquet ...
│   └── Step1X-Edit/Step1X-Edit_train_00001.parquet ...
└── val/
    ├── BAGEL/BAGEL_val_00001.parquet ...
    └── ...

Each parquet file targets ~1 GiB (roughly 370–560 samples). Filenames are <editor>_<split>_<5-digit index>.parquet; numbering restarts at 00001 for each split and each editor. Shards never mix splits, so a file can be assigned to a split by its path alone. splits.json lists every file explicitly.

Schema

One row per edited image. All images are stored as raw PNG bytes (lossless, no re-encoding) with width 1024. The dataset card declares the three image columns as Image, so the Hub viewer and datasets decode them automatically.

Column Type Description
img_name string Unique sample name / original filename, e.g. addition_100005_4_0.png
model string Editing model: BAGEL, Flux-Kontext, Qwen-Image-Edit, Step1X-Edit
split string train or val
row_index int32 0-based position within the split's original sample list
edit_type string addition, swap, or attribute_modification
edit_subtype string facial for facial attribute edits (BAGEL only), otherwise null
edit_prompt string Natural-language editing instruction
sam_prompt string Short phrase describing the edited target, used for segmentation
boxes list<list> Bounding boxes [x1, y1, x2, y2] of the edited target(s)
scores list Segmentation confidence per box
image binary Original image, PNG bytes
edited_image binary Edited image, PNG bytes
mask binary Pixel-level mask of the edited region, PNG bytes

Notes:

  • boxes / scores are the raw segmentation outputs; boxes may extend slightly beyond the image boundary.
  • Masks are 8-bit PNGs with the same spatial size as their images.

Splits

Editor Train Validation Total
BAGEL 47,320 6,000 53,320
Flux-Kontext 52,707 6,000 58,707
Qwen-Image-Edit 55,681 6,000 61,681
Step1X-Edit 51,384 6,000 57,384
All 207,092 24,000 231,092

Edit-type distribution (all splits):

Editor Addition Swap Attribute Mod.
BAGEL 25,530 16,983 10,807
Flux-Kontext 26,138 18,365 14,204
Qwen-Image-Edit 27,381 18,798 15,502
Step1X-Edit 26,070 17,370 13,944
All 105,119 71,516 54,457

The validation split follows the paper's protocol: 6,000 images are sampled from each editor's outputs, stratified to preserve that editor's edit-type composition.

Construction

  1. Source data. Real images and editing instructions are selected from OmniEdit (itself built from LAION-5B and OpenImages V6). Instructions were produced with GPT-4o. Roughly 400K image–instruction pairs were retained after filtering.
  2. Edit-type selection. Only object addition, object swap, and attribute modification are kept. Style transfer, background swap, environment change and holistic edits are excluded because they lack a unique object-localized region.
  3. Editing. Each edit-type subset is evenly divided among the four editors. All images are resized to width ~1024 before editing; generation follows each model's recommended inference settings.
  4. Quality filtering. Edited images are scored for editing accuracy and background consistency; low-confidence samples are discarded.
  5. Mask generation. ChatGPT identifies the edited target; SAM3 produces a promptable segmentation for it. Samples with segmentation confidence <= 0.5 are dropped, yielding the final pixel-level masks.

Usage

Option A — datasets (recommended) 

from datasets import load_dataset, Image

# One editor (config name = editor name); image columns are decoded automatically
ds = load_dataset("MoeNew/LocateEdit-Bench", "BAGEL")
train, val = ds["train"], ds["validation"]

# All four editors merged (distinguish by the `model` column)
ds_all = load_dataset("MoeNew/LocateEdit-Bench", "all")

# If you want raw PNG bytes instead of decoded PIL images:
raw = ds["train"].cast_column("image", Image(decode=False))

Option B — explicit files (no config), e.g. for local copies 

from datasets import load_dataset

ds = load_dataset("parquet", data_files={
    "train": "train/Qwen-Image-Edit/*.parquet",
    "validation": "val/Qwen-Image-Edit/*.parquet",
})

Option C — pyarrow / Parquet directly 

import pyarrow.dataset as pads

d = pads.dataset("LocateEdit-Bench", format="parquet", partitioning=None)
tbl = d.to_table(filter=(pads.field("split") == "val"))

Because each shard contains exactly one split (and one editor), filtering on split or model prunes whole row groups without reading image bytes. Note that split/model live in the path rather than as partition columns, so the quickest selection is by file path (splits.json / manifest.json) or by a column filter.

Evaluation protocols

The paper defines two protocols and reports pixel-level and region-level metrics:

  • Protocol 1 — Full-set evaluation. Train on the composite training set (all four editors) and test on each editor's validation split.
  • Protocol 2 — Cross-editor generalization. Train on a single editor's data and test on every editor, measuring transfer to unseen editing models.
  • Metrics. Accuracy, AUC and F1 treat each pixel as a binary decision; Dice and mIoU measure region overlap. Pixel-level accuracy/AUC are dominated by the large untouched background, so Dice/mIoU are the more informative scores.

License and citation

LocateEdit-Bench is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. The source images originate from LAION-5B and OpenImages V6 via OmniEdit, and the edited images were generated with the corresponding editing models; please also respect the terms of those upstream sources and model licenses.

@article{wu2026locateeditbench,
  title   = {LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization},
  author  = {Shiyu Wu and Shuyan Li and Jing Li and Jing Liu and Yequan Wang},
  journal = {arXiv preprint arXiv:2602.05577},
  year    = {2026}
}