--- title: SIGMA_partial canonical_url: "https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial" md_url: "https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial.md" repository: PeiyuZhuang/SIGMA_partial last_updated: 2026-10-19 license: other storage_size: "100 GB" downloads: 3 stars: 0 --- # SIGMA_partial > SIGMA_partial - An open-source dataset by PeiyuZhuang on ModelScope. SIGMApartial: 100K Subset of the SIGMA-Annotated IML Dataset PeiyuZhuang/SIGMA_partial is a dataset on ModelScope. totalling 100 GB. licensed under other. - **Repository**: PeiyuZhuang/SIGMA_partial - **License**: other - **Storage size**: 100 GB - **Downloads**: 3 - **Stars**: 0 - **Last updated**: 2026-10-19 Source: https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial --- # SIGMA_partial: 100K Subset of the SIGMA-Annotated IML Dataset **SIGMA_partial contains 100,000 samples selected from the full [SIGMA dataset](https://huggingface.co/datasets/PatrickZZZ/SIGMA).** It is a medium-scale convenience subset for image manipulation localization (IML) experiments when downloading the complete release is impractical. It is not a separate dataset and introduces no annotations beyond the full SIGMA release. The subset covers the same four source corpora as the full release: AnyEdit, CrispEdit-2M, OmniEdit-Filtered-1.2M, and PromptfixData. ## Dataset Family - **Smaller subset:** 10,000 samples [ModelScope: PeiyuZhuang/SIGMA_small](https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_small) - **This repository:** 100,000-sample subset [ModelScope: PeiyuZhuang/SIGMA_partial](https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial) - **Canonical full release:** approximately 1.1 million samples [Hugging Face: PatrickZZZ/SIGMA](https://huggingface.co/datasets/PatrickZZZ/SIGMA) Use the full Hugging Face release for large-scale training and the two ModelScope subsets when a smaller download is more practical. ## What Is SIGMA? Text-driven editing corpora already contain millions of `(original image, edited image, editing instruction)` records, but they do not provide the pixel-level masks required to train IML models. SIGMA (*Semantic-Difference Instruction-Grounding Mask Annotator*) converts these records into IML supervision by combining semantic-feature differencing with an instruction-derived spatial prior. Each released sample is derived from a public editing record and adds a binary localization mask generated by SIGMA. These masks are automatic annotations, not human-drawn ground truth. Across five public benchmarks, SIGMA reaches **89.83% average F1** and **83.52% average IoU** for mask generation. The full SIGMA-annotated release contains **1,063,140 samples** spanning **24 editing-operation categories**. Fine-tuning four existing IML detectors on SIGMA annotations raises their average F1 by **20.98 points on average** across five held-out datasets. ## Files and Integrity The 100K subset is grouped by source corpus: ```text AnyEdit/ AnyEdit_part001.zip SHA256SUMS CrispEdit-2M/ CrispEdit-2M_part001.zip ... CrispEdit-2M_part004.zip SHA256SUMS OmniEdit-Filtered-1.2M/ OmniEdit-Filtered-1.2M_part001.zip ... OmniEdit-Filtered-1.2M_part006.zip SHA256SUMS PromptfixData/ PromptfixData_part001.zip SHA256SUMS ``` The numbered files are **independent ZIP archives**, not fragments of one ZIP. Do not concatenate them. Verify and extract every archive separately: ```bash cd CrispEdit-2M sha256sum -c SHA256SUMS for archive in *.zip; do unzip "$archive"; done ``` Repeat this step for the other three source directories. ## Download :modelscope-code[]{type="sdk"} :modelscope-code[]{type="git"} Because the archives are stored with Git LFS, command-line Git users should install Git LFS before cloning: ```bash git lfs install git clone https://www.modelscope.ai/datasets/PeiyuZhuang/SIGMA_partial.git ``` ## Intended Uses Appropriate uses include: - training and debugging IML models at medium scale; - studying text-driven image manipulation localization; - comparing learning strategies for automatically generated masks; - validating a pipeline before moving to the full 1.1M release. This subset is not intended to replace manually annotated evaluation benchmarks. Automatic masks can contain residual errors, especially for ambiguous instructions, extremely subtle edits, and unintended editor side-effects. ## License and Terms of Use **The SIGMA-annotated data are released for academic research only.** This repository does not replace or override the licenses of the source corpora. Users must comply with the applicable source-dataset terms and cite both SIGMA and the corresponding source dataset. - **PromptfixData:** research use only; the dataset and models trained on it may not be used commercially. Users must also follow the licenses of its source data. - **AnyEdit:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **CrispEdit-2M:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **OmniEdit-Filtered-1.2M:** [MIT](https://opensource.org/license/mit/) Source repositories: - [AnyEdit](https://huggingface.co/datasets/Bin1117/AnyEdit) - [CrispEdit-2M](https://huggingface.co/datasets/WeiChow/CrispEdit-2M) - [OmniEdit-Filtered-1.2M](https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M) - [PromptfixData](https://huggingface.co/datasets/yeates/PromptfixData) ## Citation Please cite SIGMA and the source corpus associated with each sample: ```bibtex @misc{zhuang2026sigma, title = {SIGMA: Semantic-Difference Instruction-Grounding Mask Annotator for Text-Driven Image Manipulation Localization}, author = {Zhuang, Peiyu and Yang, Jianquan and Li, Haodong and Cai, Zhuoying and Xie, Ruitao and Zeng, Jishen and Chen, Baoying and Huang, Jiwu and Cao, Xiaochun}, year = {2026}, eprint = {2605.27924}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2605.27924} } ``` ## Related Resources - [Project page](https://zhuangpeiyu.github.io/projects/SIGMA/) - [Paper](https://arxiv.org/abs/2605.27924) - [Code](https://github.com/ZhuangPeiyu/SIGMA) - [Model checkpoint](https://huggingface.co/PatrickZZZ/SIGMA)