{"url":"/dataset/imagenet-p","name":"ImageNet-P","full_name":null,"description_markdown":"**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.\r\n\r\nSource: [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/pdf/1903.12261.pdf)","description_withheld":null,"homepage":"https://github.com/hendrycks/robustness","introduced_date":"2019-03-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-neural-network-robustness-to-2","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","first_author":"Dan Hendrycks","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"},{"name":"Adversarial Attack","url":"/task/adversarial-attack","datasets_with_task":"/datasets/task/adversarial-attack"}],"languages":[],"variants":["ImageNet-P"],"data_loaders":[{"repo":"https://github.com/hendrycks/robustness","url":"https://github.com/hendrycks/robustness","frameworks":["pytorch"]}],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-imagenet-p","task":"Image Classification","dataset_variant":"ImageNet-P","rows":1,"metrics":["Top 5 Accuracy"],"first_row_in_archive_order":{"model":"SqueezeNet + Simple Bypass","paper":"/paper/squeezenet-alexnet-level-accuracy-with-50x","metrics":{"Top 5 Accuracy":"82.5%"},"code_links":[{"title":"pytorch/vision","url":"https://github.com/pytorch/vision"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob"},{"title":"DeepScale/SqueezeNet","url":"https://github.com/DeepScale/SqueezeNet"},{"title":"jiweibo/imagenet","url":"https://github.com/jiweibo/imagenet"},{"title":"rcmalli/keras-squeezenet","url":"https://github.com/rcmalli/keras-squeezenet"},{"title":"songhan/SqueezeNet-Deep-Compression","url":"https://github.com/songhan/SqueezeNet-Deep-Compression"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/squeezenet"},{"title":"mindspore-ecosystem/mindcv","url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/squeezenet.py"},{"title":"mindlab-ai/mindcv","url":"https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/squeezenet.py"},{"title":"DT42/squeezenet_demo","url":"https://github.com/DT42/squeezenet_demo"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/squeezenet"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"Element-Research/dpnn","url":"https://github.com/Element-Research/dpnn"},{"title":"dividiti/ck-caffe","url":"https://github.com/dividiti/ck-caffe"},{"title":"vonclites/squeezenet","url":"https://github.com/vonclites/squeezenet"},{"title":"marload/ConvNets-TensorFlow2","url":"https://github.com/marload/ConvNets-TensorFlow2"},{"title":"gsp-27/pytorch_Squeezenet","url":"https://github.com/gsp-27/pytorch_Squeezenet"},{"title":"lizeng614/SqueezeNet-Neural-Style-Pytorch","url":"https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch"},{"title":"mtmd/Mobile_ConvNet","url":"https://github.com/mtmd/Mobile_ConvNet"},{"title":"Kaido0/Brain-Tissue-Segment-Keras","url":"https://github.com/Kaido0/Brain-Tissue-Segment-Keras"},{"title":"matteo-rizzo/fc4-pytorch","url":"https://github.com/matteo-rizzo/fc4-pytorch"},{"title":"avoroshilov/tf-squeezenet","url":"https://github.com/avoroshilov/tf-squeezenet"},{"title":"ejlb/squeezenet-chainer","url":"https://github.com/ejlb/squeezenet-chainer"},{"title":"haria/SqueezeNet","url":"https://github.com/haria/SqueezeNet"},{"title":"cmasch/squeezenet","url":"https://github.com/cmasch/squeezenet"},{"title":"milliemince/eBay-shipping-predictions","url":"https://github.com/milliemince/eBay-shipping-predictions"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"Dawars/SqueezeNet-tf","url":"https://github.com/Dawars/SqueezeNet-tf"},{"title":"Banus/caffe-demo","url":"https://github.com/Banus/caffe-demo"},{"title":"zjZSTU/LightWeightCNN","url":"https://github.com/zjZSTU/LightWeightCNN"},{"title":"deep-learning-algorithm/LightWeightCNN","url":"https://github.com/deep-learning-algorithm/LightWeightCNN"},{"title":"KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB","url":"https://github.com/KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB"},{"title":"mindspore-courses/heads-on-mindspore","url":"https://github.com/mindspore-courses/heads-on-mindspore/blob/main/1-best-practice/models"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/squeezenet"},{"title":"MrRen-sdhm/Embedded_Multi_Object_Detection_CNN","url":"https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN"},{"title":"King-Otaku/Emotion_Recognition_DNN","url":"https://github.com/King-Otaku/Emotion_Recognition_DNN"},{"title":"xin-w8023/SqueezeNet-PyTorch","url":"https://github.com/xin-w8023/SqueezeNet-PyTorch"},{"title":"Qengineering/SqueezeNet-ncnn","url":"https://github.com/Qengineering/SqueezeNet-ncnn"},{"title":"m1lhaus/SimpleSqueezeNet","url":"https://github.com/m1lhaus/SimpleSqueezeNet"},{"title":"2023-MindSpore-1/ms-code-217","url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/squeezenet"},{"title":"brianjychan/landuse","url":"https://github.com/brianjychan/landuse"},{"title":"AlexandruBurlacu/keras_squeezenet","url":"https://github.com/AlexandruBurlacu/keras_squeezenet"},{"title":"taltole/LiteNetwork_Image_Classification","url":"https://github.com/taltole/LiteNetwork_Image_Classification"},{"title":"taltole/CIFAR10_SqueezeNet","url":"https://github.com/taltole/CIFAR10_SqueezeNet"},{"title":"maxemerling/COVID_CT","url":"https://github.com/maxemerling/COVID_CT"},{"title":"Mind23-2/MindCode-116","url":"https://github.com/Mind23-2/MindCode-116"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/squeezenet"},{"title":"adeely9/experiment_2_python3","url":"https://github.com/adeely9/experiment_2_python3"},{"title":"vibhu444/alexnet-squeeze-mnist","url":"https://github.com/vibhu444/alexnet-squeeze-mnist"},{"title":"bogireddytejareddy/3d-squeezenet","url":"https://github.com/bogireddytejareddy/3d-squeezenet"},{"title":"code-implementation1/Code8","url":"https://github.com/code-implementation1/Code8/tree/main/squeezenet"},{"title":"mdsarfarazulh/fire-module","url":"https://github.com/mdsarfarazulh/fire-module"},{"title":"johngear/eecs504","url":"https://github.com/johngear/eecs504"},{"title":"Jastot/Rodinka_Neural_Network","url":"https://github.com/Jastot/Rodinka_Neural_Network"},{"title":"kingcong/squeezenet","url":"https://github.com/kingcong/squeezenet"},{"title":"Goandwanderfaraway/squeezenet-mindspore","url":"https://github.com/Goandwanderfaraway/squeezenet-mindspore"},{"title":"modelhub-ai/squeezenet","url":"https://github.com/modelhub-ai/squeezenet"},{"title":"Mind23-2/MindCode-80","url":"https://github.com/Mind23-2/MindCode-80"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/squeezenet-alexnet-level-accuracy-with-50x","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","date":"2016-02-24","rows_on_this_dataset":1,"code_links":59,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}