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WNJXYK/TTA-ImageNet-C

TTA-ImageNet-C Mirror of ImageNet-C (Hendrycks & Dietterich, ICLR 2019) with a revision pin for reproducible test-time adaptation evaluation. Upstream: Zenodo record 2235448 License: CC BY 4.0 (matches upstream) Maintained as part of: TTA-Evaluation-Harness Citation @inproceedings{hendrycks2019benchmarking, title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations}, author={Hendrycks, Dan and Dietterich, Thomas}… See the full description on the dataset page: https://huggingface.co/datasets/WNJXYK/TTA-ImageNet-C.

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Dataset Card

TTA-ImageNet-C

Mirror of ImageNet-C (Hendrycks & Dietterich, ICLR 2019) with a revision pin for reproducible test-time adaptation evaluation.

Citation

bibtex
@inproceedings{hendrycks2019benchmarking,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Hendrycks, Dan and Dietterich, Thomas},
  booktitle={ICLR},
  year={2019}
}

Structure

  • —15 configs: one per corruption type (gaussian_noise, shot_noise, ..., jpeg_compression).
  • —5 splits per config: severity_1 through severity_5, 50 000 images each (1000 classes x 50).
  • —Labels are ClassLabel with 1000 WordNet-ID names in torchvision order (lexicographic on wnid; n01440764 = idx 0 = tench).

Usage

python
from datasets import load_dataset

ds = load_dataset("WNJXYK/TTA-ImageNet-C",
                  name="gaussian_noise",
                  split="severity_5",
                  revision="v1.0")

Provenance

This mirror was built by scripts/publish_imagenetc.py in the TTA-Evaluation-Harness repo. JPEG bytes are copied 1:1 from the upstream files - no re-encoding, pixel-for-pixel identical to Hendrycks's release.