ImageNet-LC

This repository points to the official implementation for our paper:

ImageNet-LC: Assessing Robustness under Localized Corruptions
ICPR 2026

ImageNet-LC is an object-centric robustness benchmark for ImageNet. Instead of applying corruptions globally to the full image, ImageNet-LC localizes the foreground object regions and applies corruptions only inside those regions of interest. This enables evaluation of model robustness under more targeted, localized visual degradations.

The benchmark includes localized corruptions such as lens flare, illumination variation, dust and scratches, fingerprint smudges, focus shift, occlusion, and camouflage across multiple severity levels.

Official Code

The main code release is available here:

https://github.com/muskanny/ImageNet-LC

Please use the main repository for installation instructions, pipeline usage, updates, and issue tracking.

Dataset

The ImageNet-LC dataset is available on Kaggle:

https://www.kaggle.com/datasets/sanchitgupta10/imagenet-pc

Citation

If you use ImageNet-LC in your work, please cite the paper:

@inproceedings{imagenetlc2026,
  title     = {ImageNet-LC: Assessing Robustness under Localized Corruptions},
  booktitle = {ICPR},
  year      = {2026}
}