Selective Kernel Networks

CVPR 2019  ·  Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang ·

In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at

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Results from the Paper

Ranked #95 on Image Classification on CIFAR-100 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Image Classification CIFAR-10 SKNet-29 (ResNeXt-29, 16×32d) Percentage correct 96.53 # 103
Top-1 Accuracy 96.53 # 23
Image Classification CIFAR-100 SKNet-29 (ResNeXt-29, 16×32d) Percentage correct 82.67 # 95
Image Classification ImageNet SKNet-101 Top 1 Accuracy 79.81% # 602
Number of params 48.9M # 637
GFLOPs 8.46 # 258