Datasets › ImageNet-P

ImageNet-P

Introduced by Dan Hendrycks et al. in Benchmarking Neural Network Robustness to Common Corruptions and Perturbations28 Mar 2019 archive 2025-07-28

ImageNet-P consists of noise, blur, weather, and digital distortions. The dataset has validation perturbations; has difficulty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64, standard, and Inception-sized editions; and has been designed for benchmarking not training networks. ImageNet-P departs from ImageNet-C by having perturbation sequences generated from each ImageNet validation image. Each sequence contains more than 30 frames, so to counteract an increase in dataset size and evaluation time only 10 common perturbations are used.

Source: Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Image Classification ImageNet-P SqueezeNet + Simple Bypass Top 5 Accuracy 82.5% SqueezeNet: AlexNet-level accuracy with 50x fewer... pytorch/vision +58 1 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 32. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size 59 1 24 Feb 2016 ran 4 of 4 samples (0 unverified; 2 pointer-only for licence)

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • ImageNet-P

1 variant name, as the archive lists them.

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