In this article, we will discuss how to create an image recognition model. Also, let us learn information about image recognition.
Learn To Create Image Recognition Model
What is an image recognition model? The image recognition model is a machine learning model that helps to identify the objects within a given image.
So the image recognition model recognizes the images based on their characteristics. What is Image Recognition?
Image recognition is a process to identify the objects present in an image. It also includes the activities of understanding and classifying the objects in an image.
Some examples of image recognition are:
Recognizing the number plate of a vehicle in a picture.
Identifying a person from a photograph.
How Does Image Recognition Work?
Image recognition works on two different approaches. That is the Supervised learning approach and the Unsupervised learning approach.
Let us know in detail way from the following:
Supervised Learning Approach
In the supervised learning approach, we need to define a set of training data for each class. This data includes an input image and its category label.
The input images may be in RGB or grayscale format. Also, any other format depending on your requirement.
For example, if you want to recognize digits from images. Then you can train the dataset.
So there are five categories in our training dataset. These categories are based on images of digits 0-9.
To train our model, we need to feed this training dataset to our model. So that it can learn how to recognize these digits.
But what if we want to recognize multiple different numbers? For example, do we want to recognize both numbers and alphabets?
If this is the case, then you need to collect a separate dataset for each category label (or class label). This means that if you want 20 classes, then you need to collect 20*20.
So it is equal to 400 different datasets for training your model.
Unsupervised Learning Approach
In an unsupervised learning approach, there is no predefined set of training data needed. Instead of this, all available data will be used for training our model without any labels (without specifying the class).
So, how does unsupervised learning work? Well, before starting with unsupervised learning.
Let us understand some important terminologies used in unsupervised learning. So Instance and An instance represent one data point or object found in an input data set (image).
Each instance will have different values for its attributes (or properties) like shape, size, etc. For example, if we are trying to identify digit 0.
Then each instance will represent digit 0 with different values for its attributes like size, shape, etc. An attribute of an Instance
For example, if you want to identify the number 0 from an image. Then you can define its shape as a circle or square etc.
Also, define its size as small or large. Also, define the color of the image as black or white, etc.
A Class is a group of instances having the same properties (or attributes). So each class is a set of instances with the same values for its attributes.
For example, if you want to recognize numbers like 0-9. Then you can create ten classes based on different values for their attributes like shape, size, etc.
