Datasets:
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
image-editing
instruction-based-editing
forgery-localization
manipulation-localization
benchmark
License:
| 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: | |
| - 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: | |
| - config_name: Flux-Kontext | |
| features: | |
| - config_name: Qwen-Image-Edit | |
| features: | |
| - config_name: Step1X-Edit | |
| features: | |
| # 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](https://arxiv.org/abs/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](https://huggingface.co/datasets/codefuse-ai/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<float64>> | Bounding boxes `[x1, y1, x2, y2]` of the edited target(s) | | |
| | `scores` | list<float64> | 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) | |
| ```python | |
| 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 | |
| ```python | |
| 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 | |
| ```python | |
| 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. | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |