hendrycks/robustness resource

Health v2 · maintenance only

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1172 stars · 149 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Datasets and evaluation code for benchmarking neural network robustness to common corruptions and perturbations, from the ICLR 2019 paper by Hendrycks and Dietterich. It provides ImageNet-C, Tiny ImageNet-C, and CIFAR-10/100-C corruption benchmarks plus a leaderboard of model robustness results.

Use cases

• evaluate image classifier robustness to common corruptions
• download imagenet-c corrupted image datasets
• benchmark my resnet-50 on imagenet-c mean corruption error
• test model robustness to gaussian noise and blur perturbations
• compare corruption robustness of cnn training methods
• run cifar-10-c robustness evaluation in pytorch

When to choose

• you need standardized corruption benchmarks like ImageNet-C or CIFAR-10-C
• you want to compare your model's mCE against published robustness results
• you are doing ML safety or domain generalization research in PyTorch

When to avoid

• you need adversarial (worst-case) attack robustness rather than common corruptions
• you work outside image classification or non-PyTorch frameworks
• you need actively maintained tooling rather than a research artifact

Facets

dataset · maturity maintenance

benchmarking machine-learning computer-vision image-processing testing machine-learning deep-learning computer-vision artificial-intelligence python windows imagenet-c robustness corruptions pytorch benchmark-datasets ml-safety domain-generalization iclr-2019 linux macos

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markdown · JSON · MCP: product_card(name="hendrycks/robustness")