Datasets:

a1557811266
/

Inter-Edit-Test

This repository hosts the public release of Inter-Edit-Test, a human-annotated benchmark for the Interactive Instruction-based Image Editing (I^3E) task.

Each sample contains:

a source image,
a coarse user-style interaction mask,
a concise editing instruction,
and a ground-truth edited image.

To simplify large-scale distribution on Hugging Face, image assets are packaged as tar archives while metadata remains directly accessible.

Highlights

6,250 human-annotated test pairs
Bilingual instructions: English and Chinese
User-style coarse masks, rather than segmentation-perfect masks
High-resolution benchmark
Challenging subsets including artistic styles, low-resolution images, low-aesthetic images, and ambiguous multi-instance edits
Sanitized public release: all filenames are re-indexed to remove source-specific naming information

Release Statistics

Language distribution

English: 3,413
Chinese: 2,837

Edit type distribution

Remove: 1,641
Add: 1,603
Local: 1,533
Text Editing: 1,084
Texture: 389

Repository Structure

Inter-Edit-Test-HF/
├── source_images.tar
├── target_images.tar
├── masks.tar
├── metadata.json
├── metadata.jsonl
└── README.md

Archive contents use index-based sanitized names:

source_images/00000_source.xxx
target_images/00000_gt.xxx
masks/00000_mask.xxx

Metadata Format

Each record in metadata.json / metadata.jsonl contains:

index: zero-based dataset index
source_path: relative path inside the extracted archive
gt_path: relative path inside the extracted archive
mask_path: relative path inside the extracted archive
edit_type: edit category
language: instruction language
instruction: concise editing instruction

Quick Start

Extract the archives after download:

tar -xf source_images.tar
tar -xf target_images.tar
tar -xf masks.tar

Then load metadata as usual:

import json
from pathlib import Path

root = Path(".")
with open(root / "metadata.json", "r", encoding="utf-8") as f:
    meta = json.load(f)

sample = meta[0]
source = root / sample["source_path"]
target = root / sample["gt_path"]
mask = root / sample["mask_path"]
instruction = sample["instruction"]

Benchmark Intent

Inter-Edit-Test evaluates whether a model can:

correctly understand a concise edit instruction,
localize the intended region from an imprecise user mask,
perform the desired edit inside the target area,
preserve and harmonize the surrounding image naturally.

Project Page

GitHub: https://github.com/Delong-liu-bupt/Inter-Edit

Citation

If you use Inter-Edit-Test in your research, please cite:

@inproceedings{liu2026interedit,
  title     = {Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing},
  author    = {Liu, Delong and Hou, Haotian and Hou, Zhaohui and Huang, Zhiyuan and Han, Shihao and Zhan, Mingjie and Zhao, Zhicheng and Su, Fei},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}