Papers › Aggregated Residual Transformations for Deep Neural Networks

Aggregated Residual Transformations for Deep Neural Networks

16 Nov 2016CVPR 2017 7arXiv:1611.05431archive 2025-07-28

Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He

We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call "cardinality" (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online.

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facebookresearch/ResNeXt officialmentioned in papermentioned on GitHubtorchNOASSERTION report
D-X-Y/ResNeXt-DenseNet mentioned on GitHubpytorchMIT report
Deci-AI/super-gradients mentioned on GitHubpytorch report
Duplums/bhb10k-dl-benchmark mentioned on GitHubpytorch report
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JianGoForIt/YellowFin_Pytorch mentioned on GitHubpytorch report
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developer0hye/SKNet-PyTorch mentioned on GitHubpytorch report
facebookresearch/pycls mentioned on GitHubpytorch report
hsd1503/resnet1d mentioned on GitHubpytorch report
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jiajunhua/facebookresearch-Detectron mentioned on GitHubcaffe2 report
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kobiso/CBAM-tensorflow-slim mentioned on GitHubtfMIT report
mindspore-courses/MindSpore-classification mentioned on GitHubmindsporeMIT report
mlvccn/bmtc_transferattackvid mentioned on GitHubpytorch report
open-edge-platform/geti mentioned on GitHubpytorchApache-2.0 report
osmr/imgclsmob mentioned on GitHubmxnetMIT report
prlz77/ResNeXt.pytorch mentioned on GitHubpytorch report
tensorpack/tensorpack mentioned on GitHubtf report
wwwuyijia/mindspore_models-ResNeXt50 mentioned on GitHubmindsporeApache-2.0 report
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Tasks

Domain GeneralizationGeneral ClassificationImage Classificationimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Generalization VizWiz-Classification ResNeXt-101 32x16d Accuracy - All Images 51.7 #3 of 90 Archive leaderboard report
Domain Generalization VizWiz-Classification ResNeXt-101 32x16d Accuracy - Clean Images 54.8 #3 of 90 Archive leaderboard report
Domain Generalization VizWiz-Classification ResNeXt-101 32x16d Accuracy - Corrupted Images 48.1 #3 of 90 Archive leaderboard report
Image Classification GasHisSDB ResNeXt-50-32x4d Accuracy 98.59 #3 of 8 Archive leaderboard report
Image Classification GasHisSDB ResNeXt-50-32x4d F1-Score 99.25 #3 of 8 Archive leaderboard report
Image Classification GasHisSDB ResNeXt-50-32x4d Precision 99.94 #3 of 8 Archive leaderboard report
Image Classification ImageNet ResNeXt-101 64x4 GFLOPs 31.5 #677 of 1060 Archive leaderboard report
Image Classification ImageNet ResNeXt-101 64x4 Number of params 83.6M #677 of 1060 Archive leaderboard report
Image Classification ImageNet ResNeXt-101 64x4 Top 1 Accuracy 80.9% #677 of 1060 Archive leaderboard report
Image Classification ImageNet ResNeXt-101 64x4 Top 5 Accuracy 94.7 #677 of 1060 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: ResNeXt, ResNeXt Block

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingGrouped ConvolutionKaiming InitializationMax PoolingRandom Horizontal FlipRandom Resized CropReLUResNeXtResNeXt BlockResidual BlockResidual ConnectionSGD with MomentumStep DecayWeight Decay

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections