Add dataset card

README.md

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+---
+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
+---
+
+# 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.
+
+| 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)
+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")
+
+# Decode the PNG byte columns into PIL images when needed
+train = train.cast_column("image", Image())
+train = train.cast_column("edited_image", Image())
+train = train.cast_column("mask", Image())
+```
+
+### 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.
+- **Robustness.** Evaluation can additionally be performed under JPEG compression
+  (quality 0.75) and Gaussian blurring (sigma = 1.0).
+
+## Known issues
+
+- For 12 samples in Qwen-Image-Edit, the source metadata omitted `img_name` and
+  `edit_type`. These fields were recovered from the filename (all 12 are
+  `addition` samples); no other field is affected.
+- `boxes` / `scores` are detector/segmenter outputs and can include coordinates
+  slightly outside the image, or several boxes for one instruction.
+
+## 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  = {Wu, Shiyu and Li, Shuyan and Li, Jing and Liu, Jing and Wang, Yequan},
+  journal = {arXiv preprint arXiv:2602.05577},
+  year    = {2026}
+}
+```
+
+## Contact
+
+- Shiyu Wu, Jing Liu (jliu@nlpr.ia.ac.cn), Yequan Wang (tshwangyequan@gmail.com)
+- Issues and questions: please open an issue in this repository.