CNN Architecture Explained: What It Means In Deep Learning?

Introduction to CNN Architecture 

Before we go deeper into the Image Classification of CNN Architecture, let us first look into “what is CNN architecture?” CNN or Conventional Neural Network is a set of neural networks that can extract unique features from an image. A perfect example of CNN or Conventional Neural Network is face detection and recognition, as they can easily classify complex features in image data. Now the question also arises, how do these work?  

The Conventional Neural Network (CNN) can consume numerical data. For this, the images that are fed to these networks require conversion into numerical data. We very well know that the images are constituted of pixels. These pixels are then converted to numerical data form when passed to CNN.  

We now understand what CNN is. Let us now look into CNN Architecture – Image Classification.  

CNN Model for Image Classification 

You must have heard about Computer Vision. Computer Vision is a domain that focuses on the fundamental problem of training a computer to watch human activities and how a human does it. Computer Vision tasks are performed by many smartphone applications these days, such as Amazon Go Store, Mitek systems, etc. We all have smartphones, and there is no shortage of these applications today that perform such tasks.  

It is not really an easy task for the way in which we become conscious of the world; it cannot be done with a few lines of coding. We have to research the path of development by constantly recognizing, segmenting & inferring objects that pass through our vision which is taking in information. Computer Vision studies the phenomenon of human vision by tackling various tasks. A few of the tasks are as follows: 

• Object Detection 
• Image Classification 
• Image Reconstruction 
• Face Recognition 
• Semantic Segmentation 

With the growth of the digital age, the research behind these tasks is rapidly growing. This is much better than deep learning.  

Image classification aims to relate each image to a certain set of class labels. In this administer learning issue, a set of pre-labeled training data is provided to a Machine Learning algorithm. This algorithm makes an effort to recognize the visual characteristics present in the training photos linked to each label and categorizes unlabeled images accordingly. Today, we will investigate this widely used problem using the Kera Open-Source Library for Deep Learning. 

Let’s understand how these work:  

Neural Network Architecture 

Neural Network Layers 

These are made up of a single input, a hidden layer, and a single output layer. The dense hidden layers are fed values from the input vector from the input layers, which are made up of nodes. Depending on the type of data and the categorization issue, there may be a lot of hidden layers. A single node in one layer is connected to all nodes in the subsequent layer by a number of channels, making the hidden layers fully connected. Up until they arrive at the Output layer, input values are relayed forward. The nodes in the output layer belong to the classes that the network is predicting. 

Forward Propagation 

When the data is passed onto the network, it is spread through various channels that connect Input, Hidden, and Output layers. The values of these data are converted within these hidden layers.  

Backpropagation  

The neuron would be the anticipated class by the model once it reaches the Output layer. The loss is computed based on the network’s output, and the amount of modification that must take place in the output layer to reduce loss is determined. In order to increase the output node according to the appropriate class, neural networks try. Through backpropagation, this is accomplished. The gradients of the loss function with respect to the weights of each hidden layer are used in backpropagation to raise the value of the right output node. 

How Does a Computer See These Images? 

With the aid of an illustration, we will better comprehend this classification. Here, it’s important to comprehend how picture classification functions and how data is conveyed using images. We’ll use a fruit bowl as an example. Think of a fruit bowl. The situation is considerably different from what we see with an algorithm. Pixel grids make up digital images when we talk about them. The values of the picture pixels, which describe the level of light, range from 0 to 255. The axes are represented by the matrices X and Y. 

We are aware that color images have a third dimension called depth and are made up of the RGB model. A 3-Dimensional matrix of red, green, and blue light-intensity values makes up a color image. This fruit bowl image has the following measurements: 400 x 682 x 3. The dimensions are 682 pixels wide and 400 pixels tall. Since this is an RGB image, the depth is 3. 

Convolutional Neural Learning for Image Classification 

Convolutional Neural Networks (CNNs) have been a popular approach to resolving this issue. Today, almost all Computer Vision applications are powered by this subclass of deep neural networks. Nowadays, everybody with a smartphone has access to high-resolution pictures. 

Segmentation and pictures are the topics of Computer Vision. One of the most crucial steps in the procedure is this one. The segmentation of the visual input simplifies the analysis. Sets of one or more pixels make up segments. The segmentation of the image divides pixels into more substantial parts. The division of the image into tiles takes place in units. Small areas on an image that shouldn’t be separated are first defined as part of the image segmentation procedure. The location of these zones, which are referred to as seeds, determines the tile arrangement.  

Image segmentation has two levels of granularity. They are as follows: 

• Semantic segmentation: Semantic segmentation divides image pixels into a few classes that can be understood semantically as representing actual objects. Region proposal and annotation is the process of utilizing CNN to classify the pixel values into discrete groups. Candidate object patches (COMPs), which can be considered as little clusters of pixels that most likely belong to the same item, are another name for region proposals. 
• Segmenting by instances: In this example, each instance of each item is identified. Semantic segmentation doesn’t classify every pixel, distinguishing it from instance segmentation. In the case of three identical items (such as bicycles) in an image, instance segmentation identifies each individual bicycle, while semantic segmentation classifies all the bicycles as a single instance.

Conclusion 

You learned about the fundamental components of convolutional neural networks in this post, along with various regularization techniques. Use the architecture described in this article as a guide and alter it as necessary. You should walk away from it with a basic knowledge of CNNs and a functional startup model. To see how this affects performance, try experimenting yourself with adding or removing convolutional layers and modifying the dropout or learning rates! UNext Jigsaw is offering its signature PG Certificate Program in Data Science and Machine Learning with a guaranteed placement* if you’re interested in a successful career in Data Science and ML.