Training Machine Learning Models With Image Annotation


Introduction

Let’s suppose a situation. You get a project in which you have to build an AI that can detect if a door is open or closed by using an image. Now, computers are dumb. Like really dumb. The computer doesn’t know what an open door looks like, or what a closed-door looks like. 

So to build a model like that, you have to spoon-feed the model about these two different types of images. And to train a model, you need high-quality dataset. You collected hundreds if not thousands of images with both open and closed doors. Now, to make the model understand the different doors, you have to annotate (or label) each image, with an open and closed door in order to train the AI model. 

Image annotation is important for the model to perform tasks like image classification, object detection and image segmentation. This article covers topics like what is image annotation, why is it important, what are the types, applications, and more. 

What is Image Annotation?

Image data annotation or image annotation is a process by which annotators label the objects in an image in order to make the AI model recognize them in unlabeled images. This is done to detect, classify and group the different objects in a machine learning algorithm for effective learning of data. 

Image annotation sets the foundation behind many AI products that you interact with. In image annotation, annotators or data labelers draw shapes, drawings or comments on an object to label it. 

Why Image Annotation is important?

With the growing demand for machine learning and AI applications, as well as the progressive effect of computer vision technology implementation into the untapped field of image/video processing, image annotation has become a critical practice for improving the performance and efficiency of existing machine learning models. 

With Image Annotation you can:

  • Identify and detect objects of the interest in the image
  • Classify the different objects in the image
  • Recognize the different classes of objects
  • Improve the accuracy, efficiency and performance of your existing models, and
  • It helps the machine learning model easily detect and classify objects. 

There are two most important objectives when it comes to image annotation. The first is to assist machine learning and AI models in the labelling of datasets, image classification and regression for a given dataset much easier and more accurate. Secondly, image annotation is heavily used to validate the model’s ability to detect, recognize and classify objects precisely while testing any AI or machine learning model. 

As a result, image annotation is crucial when using computer vision for image processing. The quality of the machine learning model, on the other hand, is entirely dependent on the quality of training data and the engineers who worked on it.

What are the types of Image Annotation?

There are four types of classification, and the type and complexity of the project will decide the type of image annotation you will use. 

1. Image classification

This is a type of machine learning model where an image has a single object. The image classification aims at detecting the object in the image, not the location. Suppose you have an image in which a cat can be seen in a sitting position. In classification, you don’t label where the cat is, you just tell the computer to recognise the presence of a cat in this picture. 

2. Object detection

In Object detection, there are more variables like finding the presence, location and number of objects in the image. Here, annotators draw boxes around the objects which allows the model to know the location as well as the number of objects in the frame. 

3. Image segmentation

Image segmentation is a type of technique where the object is annotated pixel by pixel. There are three parts of image segmentation, namely, semantic segmentation, instance segmentation, and panoptic segmentation. 

4. Object tracking

Once the object is identified, object tracking is used to track the position of an object in consecutive frames, like in a video. Object movement or tracking can be studied by using surveillance video footage or camera footage. 

What are the applications of Image Annotation?

There are many industries that make use of image annotation. But here, we will focus on these five industries. 

1. Agriculture

Data that is collected from drones or satellites can help farmers help in things like estimating crop fields, evaluating soil, and more. A great example of this would be annotating data to identify the difference between crops and weeds. Farmers then will use this data to pesticide the areas affected with weed and this can help save a huge amount of money. 

2. Healthcare

Doctors are using AI-powered solutions to supplement their diagnoses. For example, AI can analyse radiology images to determine whether certain cancers are present or not. Like, researchers can use thousands of scans labelled with cancerous and non-cancerous spots to train a model until it distinguishes between both on its own. While AI isn’t meant to replace doctors, it can be used to double-check and improve the accuracy of important medical decisions. 

3. Manufacturing

In manufacturing, AI can help retailers capture information about inventory. This data can tell the managers if a particular product is out-of-stock, needs additional units, or more. One more application of this can be checking different types of equipment for failures, damages, or faults. 

4. Finance 

A great application of image annotation in finance is to identify faces using facial recognition while a person is withdrawing money from the bank. This would help significantly in reducing crimes and fraud. 

5. Retail

There are many industries that require image annotation. If you want to use AI to deliver the most relevant results for a specific item such as jeans, you need to build a model that can look through a product catalogue and serve results that the user wants, for this purpose image annotation is required. 

In addition, several retailers are testing robots in their stores. These robots take pictures of shelves to see if a product is less in stock or out of stock, indicating that it needs to be reordered. These robots can also use image transcription to gather product information by scanning barcode images. 

What GTS can do for you?

AT Global Technology Solutions, we understand that you need a high-quality AI training dataset in order to train, test, and validate the data. And that’s why we provide different annotation services like image annotation, video annotation, text annotation and speech annotation. We also offer different services like data collection, annotation and robotic process automation. 

Our expert team have the required resources and expertise to be able to handle and deliver your project on time. Our work is efficient, accurate and up to the mark. No matter what your demand is, we are always ready to help you in making the AI and ML projects. 

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