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Dec 28, 2023 · X ception, an abbreviation for “ Extreme Inception ,” represents a milestone in convolutional neural network (CNN) design. Conceived by François Chollet, the creator of the Keras deep learning...
Oct 21, 2020 · The Conv-Batch Norm block takes as inputs, a tensor — x, number of filters — filters, kernel size of the convolutional layer — kernel_size, strides of convolutional layer — strides. Then we apply a convolution layer to x and then apply Batch Normalization.
- The Limits of Convolutions
- The Depthwise Convolution
- Pointwise Convolution
- Implementation of The Xception
First of all, let’s take a look at convolutions. Convolution is a really expensive operation. Let’s illustrate this : The input image has a certain number of channels C, say 3 for a color image. It also has a certain dimension A, say 100 * 100. We apply on it a convolution filter of size d*d, say 3*3. Here is the convolution process illustrated : N...
Depthwise Convolution is a first step in which instead of applying convolution of size d×d×Cd×d×C, we apply a convolution of size d×d×1d×d×1. In other words, we don’t make the convolution computation over all the channels, but only 1 by 1. Here is an illustration of the Depthwise convolution process : This creates a first volume that has size K×K×C...
Pointwise convolution operates a classical convolution, with size 1×1×N1×1×N over the K×K×CK×K×C volume. This allows creating a volume of shape K×K×NK×K×N, as previously. Here is an illustration of the Pointwise Convolution : Alright, this whole thing looks fancy, but did we reduce the number of operations? Yes we did, by a factor proportional to 1...
XCeption offers an architecture that is made of Depthwise Separable Convolution blocks + Maxpooling, all linked with shortcuts as in ResNet implementations. The specificity of XCeption is that the Depthwise Convolution is not followed by a Pointwise Convolution, but the order is reversed, as in this example :
Sep 25, 2018 · In this story, Xception [1] by Google, stands for Extreme version of Inception, is reviewed. With a modified depthwise separable convolution, it is even better than Inception-v3 [2] (also by Google, 1st Runner Up in ILSVRC 2015) for both ImageNet ILSVRC and JFT datasets.
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keras.applications.Xception( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", ) Instantiates the Xception architecture. Reference. Xception: Deep Learning with Depthwise Separable Convolutions (CVPR 2017)
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Xception is a novel deep convolutional neural network architecture, where Inception modules have been replaced with depthwise separable convolutions. With a similar parameter count, Xception significantly outperforms Inception V3 on a larger image classification dataset called JFT.
