Papers › Aggregated Residual Transformations for Deep Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
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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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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