Xception: Deep Learning with Depthwise Separable Convolutions

CVPR 2017 François Chollet

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions.

Full paper


Task Dataset Model Metric name Metric value Global rank Compare
Image Classification ImageNet Xception Top 1 Accuracy 79% # 10
Image Classification ImageNet Xception Top 5 Accuracy 94.5% # 10