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... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Image Classification ImageNet Xception Top 1 Accuracy 79% # 59
Image Classification ImageNet Xception Top 5 Accuracy 94.5% # 47
Image Classification ImageNet Xception Number of params 22.8M # 1