| # Inter-Edit-Test |
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| Official test benchmark release for the CVPR 2026 paper: |
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| **Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing** |
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| This repository hosts the public release of **Inter-Edit-Test**, a human-annotated benchmark for the Interactive Instruction-based Image Editing (`I^3E`) task. |
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| Each sample contains: |
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| - a source image, |
| - a coarse user-style interaction mask, |
| - a concise editing instruction, |
| - and a ground-truth edited image. |
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| To simplify large-scale distribution on Hugging Face, image assets are packaged as tar archives while metadata remains directly accessible. |
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| ## Highlights |
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| - **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 |
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| ## Release Statistics |
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| ### Language distribution |
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| - English: **3,413** |
| - Chinese: **2,837** |
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| ### Edit type distribution |
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| - Remove: **1,641** |
| - Add: **1,603** |
| - Local: **1,533** |
| - Text Editing: **1,084** |
| - Texture: **389** |
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| ## Repository Structure |
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| ```text |
| Inter-Edit-Test-HF/ |
| ├── source_images.tar |
| ├── target_images.tar |
| ├── masks.tar |
| ├── metadata.json |
| ├── metadata.jsonl |
| └── README.md |
| ``` |
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| Archive contents use index-based sanitized names: |
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| - `source_images/00000_source.xxx` |
| - `target_images/00000_gt.xxx` |
| - `masks/00000_mask.xxx` |
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| ## Metadata Format |
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| Each record in `metadata.json` / `metadata.jsonl` contains: |
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| - `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 |
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| ## Quick Start |
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| Extract the archives after download: |
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| ```bash |
| tar -xf source_images.tar |
| tar -xf target_images.tar |
| tar -xf masks.tar |
| ``` |
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| Then load metadata as usual: |
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| ```python |
| import json |
| from pathlib import Path |
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| root = Path(".") |
| with open(root / "metadata.json", "r", encoding="utf-8") as f: |
| meta = json.load(f) |
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| sample = meta[0] |
| source = root / sample["source_path"] |
| target = root / sample["gt_path"] |
| mask = root / sample["mask_path"] |
| instruction = sample["instruction"] |
| ``` |
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| ## Benchmark Intent |
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| Inter-Edit-Test evaluates whether a model can: |
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| 1. correctly understand a concise edit instruction, |
| 2. localize the intended region from an imprecise user mask, |
| 3. perform the desired edit inside the target area, |
| 4. preserve and harmonize the surrounding image naturally. |
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| ## Project Page |
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| - GitHub: https://github.com/Delong-liu-bupt/Inter-Edit |
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| ## Citation |
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| If you use Inter-Edit-Test in your research, please cite: |
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| ```bibtex |
| @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} |
| } |
| ``` |
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