Aleksandar/NearID-SDXL_1024

NearID-SDXL_1024 — Near-Identity Distractors (Stable Diffusion XL inpainting)

      

This dataset contains near-identity distractors generated by Stable Diffusion XL inpainting at 1024×1024 resolution as part of the NearID project.

Each sample contains up to 3 distractor images (nimg1, nimg2, nimg3): different but visually similar instances inpainted into the exact same background/context as the corresponding anchor in the base Aleksandar/NearID dataset. These distractors are used to train and evaluate identity embeddings that distinguish true identity from contextual shortcuts.

Quick Start

from datasets import load_dataset

# Load this negative source
ds = load_dataset("Aleksandar/NearID-SDXL_1024")

# Load base positives for anchor/positive pairs
positives = load_dataset("Aleksandar/NearID")

Dataset Structure

How the Distractors Were Generated

1. 1.For each anchor identity in the base NearID dataset, a semantically similar but different object instance was retrieved.
2. 2.The distractor instance was inpainted into the same background as the anchor using Stable Diffusion XL inpainting.
3. 3.Resolution: 1024×1024 pixels.

This creates a controlled test: a model must rely on intrinsic identity features, not background context, to distinguish anchor from distractor.

All NearID Datasets

Related

• —Model: Aleksandar/nearid-siglip2 — NearID identity embedding model
• —Paper: NearID: Identity Representation Learning via Near-identity Distractors
• —Code: github.com/Gorluxor/NearID

License & Attribution

This dataset is released under CC-BY-4.0. It is derived from the SynCD dataset (MIT License, Copyright 2022 SynCD). If you use this dataset, please cite both NearID and SynCD.

Citation

@article{cvejic2026nearid,
  title={NearID: Identity Representation Learning via Near-identity Distractors},
  author={Cvejic, Aleksandar and Abdal, Rameen and Eldesokey, Abdelrahman and Ghanem, Bernard and Wonka, Peter},
  journal={arXiv preprint arXiv:2604.01973},
  year={2026}
}