a1557811266/Inter-Edit-Train

--- language: - en - zh pretty_name: Inter-Edit-Train size_categories: - 1M<n<10M task_categories: - image-to-image tags: - image-editing - benchmark - computer-vision - multimodal --- # Inter-Edit-Train Inter-Edit-Train is the official large-scale training set released for the CVPR 2026 paper **Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing**. This dataset is designed for the Interactive Instruction-based Image Editing (I^3E) task, where a model performs localized image edits from a concise textual instruction together with imprecise spatial guidance. ## Highlights - **1,099,964** image editing pairs - **610,186** unique source images - Four edit types: **Local**, **Add**, **Remove**, and **Texture** - Seven common aspect ratios from **16:9** to **9:16** - Includes edit instructions, masks, bounding boxes, and an extra `better_data` flag - All release filenames are anonymized with index-based names; original internal filenames are not exposed in the packaged archives ## Relation to the paper This release corresponds to the training split described in the paper. The released manifest keeps the fields needed for training and data usage: - `instruction` - `edit_type` - `bounding_box` - `bbox_reference_dimensions` - `better_data` - anonymized source / target / mask asset locations The key `better_data` is **not** a paper-defined benchmark field. It is an additional release-only flag indicating samples that were judged to be more suitable for training after filtering. ## Data layout Because the full training set is extremely large, the assets are released as sharded tar archives: - `source_shards/source-xxxxx-of-xxxxx.tar` - `asset_shards/asset-xxxxx-of-xxxxx.tar` - `metadata/train-xxxxx-of-xxxxx.jsonl.gz` Each asset name inside the tar archives is anonymized: - source image: `sources/source_0000000.png` - edited image: `targets/target_0000000.png` - mask image: `masks/mask_0000000.png` Each metadata row records which tar shard and which internal filename should be used for that sample. ## Metadata schema Each JSONL record contains: - `sample_id`: zero-based sample index - `source_id`: zero-based unique source-image index - `edit_type` - `instruction` - `better_data` - `bounding_box` - `bbox_reference_dimensions` - `source_archive` - `source_file` - `asset_archive` - `target_file` - `mask_file` ## Example metadata entry ```json { "sample_id": 0, "source_id": 0, "edit_type": "Add", "instruction": "添加一双发光的筷子", "better_data": false, "bounding_box": [357, 694, 902, 926], "bbox_reference_dimensions": {"width": 960, "height": 960}, "source_archive": "source_shards/source-00000-of-00245.tar", "source_file": "sources/source_0000000.png", "asset_archive": "asset_shards/asset-00000-of-00275.tar", "target_file": "targets/target_0000000.png", "mask_file": "masks/mask_0000000.png" } ``` ## Usage notes - This is the **training** release, not the manually annotated test benchmark. - The canonical sample order follows the original `Inter-Edit-train.json`. - Source images are deduplicated globally and indexed separately from sample indices. - Asset filenames are anonymized by design. ## Citation If you use this dataset, please cite: ```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}, year={2026} } ```--- 语言： - 英语 - 汉语 展示名称：Inter-Edit-Train 规模类别： - 100万 < 样本量 < 1000万 任务类别： - 图像到图像（image-to-image） 标签： - 图像编辑（image-editing） - 基准测试集（benchmark） - 计算机视觉（computer vision） - 多模态（multimodal） --- # Inter-Edit-Train Inter-Edit-Train 是为2026年国际计算机视觉与模式识别会议（CVPR）论文**Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing**发布的官方大规模训练集。 本数据集专为交互式指令式图像编辑（Interactive Instruction-based Image Editing，简称I^3E）任务设计，该任务要求模型根据简洁的文本指令与不精确的空间指导完成局部图像编辑。 ## 核心亮点 - **1,099,964** 组图像编辑样本对 - **610,186** 张独特源图像 - 四种编辑类型：**局部（Local）**、**添加（Add）**、**移除（Remove）**与**纹理（Texture）** - 七种常见宽高比，覆盖从**16:9**到**9:16** - 包含编辑指令、掩码、边界框（bounding box）以及额外的`better_data`标记 - 所有发布的文件名均采用基于索引的匿名命名方式；打包归档中未披露原始内部文件名 ## 与论文的关联 本发布对应论文中描述的训练划分。发布的元数据清单保留了训练与数据使用所需的字段： - `instruction`（编辑指令） - `edit_type`（编辑类型） - `bounding_box`（边界框） - `bbox_reference_dimensions`（边界框参考尺寸） - `better_data`（优质数据标记） - 匿名化的源图像/目标图像/掩码资源路径 关键字段`better_data`并非论文定义的基准测试字段，而是本次发布新增的专属标记，用于标识经过筛选后更适合用于训练的样本。 ## 数据布局 由于完整训练集体量庞大，相关资源以分块tar归档（tar archive）形式发布： - `source_shards/source-xxxxx-of-xxxxx.tar` - `asset_shards/asset-xxxxx-of-xxxxx.tar` - `metadata/train-xxxxx-of-xxxxx.jsonl.gz`（JSON Lines格式元数据压缩包） tar归档内的每个资源文件名均采用匿名化格式： - 源图像：`sources/source_0000000.png` - 编辑后图像：`targets/target_0000000.png` - 掩码图像：`masks/mask_0000000.png` 每条元数据行记录了该样本对应的tar分块与内部文件名。 ## 元数据架构 每条JSON Lines（JSONL）记录包含以下字段： - `sample_id`：从零开始的样本索引 - `source_id`：从零开始的唯一源图像索引 - `edit_type`（编辑类型） - `instruction`（编辑指令） - `better_data`（优质数据标记） - `bounding_box`（边界框） - `bbox_reference_dimensions`（边界框参考尺寸） - `source_archive`（源图像归档路径） - `source_file`（源图像文件路径） - `asset_archive`（资源归档路径） - `target_file`（目标图像文件路径） - `mask_file`（掩码图像文件路径） ## 元数据示例条目 json { "sample_id": 0, "source_id": 0, "edit_type": "Add", "instruction": "添加一双发光的筷子", "better_data": false, "bounding_box": [357, 694, 902, 926], "bbox_reference_dimensions": {"width": 960, "height": 960}, "source_archive": "source_shards/source-00000-of-00245.tar", "source_file": "sources/source_0000000.png", "asset_archive": "asset_shards/asset-00000-of-00275.tar", "target_file": "targets/target_0000000.png", "mask_file": "masks/mask_0000000.png" } ## 使用说明 - 本数据集为**训练集**发布，而非人工标注的测试基准集。 - 标准样本顺序遵循原始`Inter-Edit-train.json`文件。 - 源图像已进行全局去重，并独立于样本索引进行编号。 - 资源文件名采用匿名化设计。 ## 引用 若您使用本数据集，请引用以下文献： 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}, year={2026} }