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.

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Evaluation


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