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What is GoogLeNet?
GoogLeNet, which is famously known as Inception Net, is a Deep Learning model built by researchers at Google. Going Deeper with Convolutions was the paper by which the GoogleNet Model first came into existence.
The ILSVRC (ImageNet Large Scale Visual Recognition Challenge) evaluates models based on their object detection and image classification performance every year. GoogLeNet was declared the winner in ILSVRC 2014 for the image classification challenge. The performance of GoogLeNet was significantly better than VGG Model (runner-up). Also, the error rate was reduced to a much greater extent when compared with 2013 and 2012 ILSVRC winners ZF-Net and AlexNet, respectively.
GoogleNet introduced the inception module in deep learning, which we’ll study more in this blog.
GoogleNet Model Architecture
There are 22 Parameterized Layers in the Google Net architecture; these are Convolutional Layers and Fully-Connected Layers; if we include the non-parameterized layers like Max-Pooling, there are a total of 27 layers in the GoogleNet Model.
In the below architecture, every box represents a layer,
Blue Box - Convolutional Layer
Green Box - Feature Concatenation
Red Box - MaxPool Layer
Yellow Box - Softmax Layer
GoogleNet Architecture
GoogLeNet Layer Description
Input - The GoogLeNet model takes an input image of 224 x 224.
Output - The output layer (or the softmax layer) has 1000 nodes that correspond to 1000 different classes of objects.
Features of GoogleNet
GoogleNet, also known as Inception v1, is a deep convolutional neural network architecture designed by Google for image classification tasks. It won the ILSVRC 2014 competition. Here are its key features:
Inception Module
The idea of the Inception module is to bring down the number of parameters in a Deep Neural Network; the inception module is built with many small convolutions.
The models like AlexNet have 60 Million parameters, whereas GoogleNet had only 4 Million parameters also; the architecture of GoogleNet was much deeper than AlexNet.
GoogleNet model has nine identical units known as inception units or inception modules.
Inception Unit
There are Convolutional Kernels of different shapes 1x1, 3x3, & 5x5 in the Inception Module. The output of all these kernels is stacked together at the end of the inception unit. Larger convolutional kernels cover a large area in an image to get the information, and the smaller kernels work on a smaller area in the image.
So, a 1x1 convolutional kernel will give us a finer detail in the image when compared with a 5x5 convolutional kernel.
Auxiliary Classifier
Auxiliary Classifier is a classification unit added twice in the middle of the GoogleNet network. We use Auxiliary Classifiers to tackle the problem of vanishing gradient.
Auxiliary Classifier
Each auxiliary classifier has a 5x5 average pooling layer, a 1x1 convolutional layer, two fully connected layers with 1024 units, and a softmax layer with 1000 units.
The auxiliary classifier units are added on top of the middle inception units. When we train the neural network, the loss of the auxiliary classifier units is multiplied by 0.3 and then added to the total network loss.
Advantages of GoogleNet Model
In today’s world, we use GoogleNet for various Computer Vision applications, including Object Detection, Image Classification, etc.
Source: medium.com
Around 18% of the GoogleNet applications are based on Image Classification, and about 10-10% are Object Detection and Quantization.
GoogleNet has proven to be faster when compared with other image-classification models like VGG.
GoogleNet is much more concise, the size of a pre-trained VGG16 model is 528 MB, and that of a VGG19 model is 549 MB, whereas the size of a pre-trained GoogleNet is 96 MB & InceptionV3 is 92 MB.
GoogleNet achieves higher efficiency by compressing the input image and simultaneously retaining the important features/information.
Frequently Asked Questions
What are the applications of GoogleNet in today’s world?
We perform many computer-vision tasks using GoogleNet such as:
Image Classification,
Object Detection,
Object Recognition,
Quantization,
Face Recognition,
Object Classification, and many more.
How deep is the GoogleNet Neural Network?
In GoogleNet model architecture, there are 22 parameterized layers and a total of 27 layers, including the MaxPooling Layers.
What is the benefit of using Inception Units?
Inception units help decrease the number of parameters in a network, allowing us to train a deeper network.
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