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- The first version entered the field in 2014, and as the name "GoogleNet" suggests, it was developed by a team at Google.
- The GoogleNet Architecture is 22 layers deep, with 27 pooling layers included.
- GoogleNet is trained using distributed machine learning systems with a modest amount of model and data parallelism.
- From the name “GoogLeNet”, we’ve already known that it is from Google.
- To classify new images using GoogLeNet, use classify .
- You can retrain a GoogLeNet network to perform a new task using transfer learning.
- You can also create a trained GoogLeNet network from inside MATLAB by installing the Deep Learning Toolbox Model for GoogLeNet Network support package.
- If the Deep Learning Toolbox Model for GoogLeNet Network support package is not installed, then the function provides a link to the required support package in the Add-On Explorer.
- You can create an untrained GoogLeNet network from inside MATLAB by importing a trained GoogLeNet network into the Deep Network Designer App and selecting Export > Generate Code.
- The exported code will generate an untrained network with the network architecture of GoogLeNet.
- GoogLeNet introduces several new concepts.
- Another key network that we already seen on quite some occasions in this lecture is GoogleNet.
- One particular incarnation of this architecture, GoogLeNet, a 22 layers deep network, was used to assess its quality in the context of object detection and classification.
- The paper proposes a new type of architecture – GoogLeNet or Inception v1.
- Now that you have understood the architecture of GoogLeNet and the intuition behind it, it’s time to power up Python and implement our learnings using Keras!
- The GoogLeNet builds on the idea that most of the activations in a deep network are either unnecessary(value of zero) or redundant because of correlations between them.
- So GoogLeNet devised a module called inception module that approximates a sparse CNN with a normal dense construction(shown in the figure).
- Let us take the first inception module of GoogLeNet as an example which has 192 channels as input.
- Use of a large network width and depth allows GoogLeNet to remove the FC layers without affecting the accuracy.
- The most straightforward way to improve performance on deep learning is to use more layers and more data, googleNet use 9 inception modules.
- Bellow we present 2 inception layers on cascade from the original googleNet.
- 7.4.2, GoogLeNet uses a stack of a total of 9 inception blocks and global average pooling to generate its estimates.
- We can now implement GoogLeNet piece by piece.
- The GoogLeNet model is computationally complex, so it is not as easy to modify the number of channels as in VGG.
- Moreover, two popular GoogLeNet architecture versions of CNN, namely, Inception-v1 and Inception-v3, were used.
- In GoogLeNet architecture, there is a method called global average pooling is used at the end of the network.
- GoogLeNet was the winner at ILSRVRC 2014 taking 1st place in both classification an detection task.
- The design of this initial Inception Module is known commonly as GoogLeNet, or Inception v1.
- GoogLeNet uses 9 inception module and it eliminates all fully connected layers using average pooling to go from 7x7x1024 to 1x1x1024.
- from_pretrained ( "googlenet" ) model .
- A Guide to AlexNet, VGG16, and GoogleNet
- Review: GoogLeNet (Inception v1)— Winner of ILSVRC 2014 (Image Classification)
- GoogLeNet convolutional neural network
- matlab-deep-learning/googlenet: Repo for GoogLeNet
- Architectures — Part 1. From LeNet to GoogLeNet
- Going Deeper with Convolutions – Google Research
- Implementation Of GoogleNet In Keras
- ResNet, AlexNet, VGGNet, Inception: Understanding various architectures of Convolutional Networks – CV-Tricks.com
- 7.4. Networks with Parallel Concatenations (GoogLeNet) — Dive into Deep Learning 0.15.1 documentation
- Offline Signature Verification using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-v3
- Understanding GoogLeNet Model
- Inception Module
- Evolution of CNN Architectures: LeNet, AlexNet, ZFNet, GoogleNet, VGG and ResNet