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Inception v3 [1] [2] is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for GoogLeNet. It is the third edition of Google's Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge.
- Inception V1
- Inception V2
- Inception V3
- Inception V4
- Inception-ResNet V1 and V2
This is where it all started. Let us analyze what problem it was purported to solve, and how it solved it. (Paper)
Inception v2 and Inception v3 were presented in the same paper. The authors proposed a number of upgrades which increased the accuracy and reduced the computational complexity. Inception v2 explores the following:
The Premise
1. The authors noted that the auxiliary classifiers didn’t contribute much until near the end of the training process, when accuracies were nearing saturation. They argued that they function as regularizes, especially if they have BatchNorm or Dropout operations. 2. Possibilities to improve on the Inception v2 without drastically changing the modules were to be investigated.
The Solution
1. Inception Net v3incorporated all of the above upgrades stated for Inception v2, and in addition used the following: 1. RMSProp Optimizer. 2. Factorized 7x7 convolutions. 3. BatchNorm in the Auxillary Classifiers. 4. Label Smoothing (A type of regularizing component added to the loss formula that prevents the network from becoming too confident about a class. Prevents over fitting).
Inception v4 and Inception-ResNet were introduced in the same paper. For clarity, let us discuss them in separate sections.
Inspired by the performance of the ResNet, a hybrid inception module was proposed. There are two sub-versions of Inception ResNet, namely v1 and v2. Before we checkout the salient features, let us look at the minor differences between these two sub-versions. 1. Inception-ResNet v1 has a computational cost that is similar to that of Inception v3. 2....
- Bharath Raj
Instantiates the Inception v3 architecture. Reference. Rethinking the Inception Architecture for Computer Vision (CVPR 2016) This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet. For image classification use cases, see this page for detailed examples.
23 ott 2021 · 1. Inception-V3 Implemented Using Keras : To Implement This Architecture in Keras we need : Convolution Layer in Keras . tf.keras.layers.Conv2D(. filters, #Number Of Filters. kernel_size, # filter ...
tf.keras. Instantiates the Inception v3 architecture.
论文的解决方法是factorized convolution和正则化方法： Here we are exploring ways to scale up networks in ways that aim at utilizing the added computations as efficiently as possible by suitably factorized convolutions and aggressive regularization. 1. Introduction. 开始讲了Inception (指的是Inception V1)降低计算复杂度，之后说了其的缺点：
