Why does image annotation matter in the world of machine learning? The answer is simple. The images you use to train, validate, and test your algorithms will directly impact the performance of your AI project.
Every image in your datasets matters. The goal of a training dataset is to train your AI system to recognize and predict outcomes—the higher the quality of your annotations, the more accurate and precise your models are likely to be.
But image annotation isn’t always easy, especially if you’re dealing with large quantities of diverse data. Getting familiar with ML image labeling is one of the fastest ways to get to market with a high-performing, meaningful machine learning model.
Are you interested in boosting the performance of your next AI project? We’ve done the legwork and put together a comprehensive guide to image annotation types, tools, and techniques.
What Is Image Annotation?
Image annotation is the process of labeling an image to show a machine learning model which features you want it to recognize. Annotating an image creates metadata through tagging, processing, or transcribing certain objects within the image.
Training a machine learning model to recognize desired features requires the principles of supervised learning. The goal is for your machine learning model to identify desired features in a real-world environment—and make a decision or take some action as a result.
Image Annotation Types and Techniques
There are many different types of image annotations. Each one is distinct in how it classifies particular features or areas of an image. Here are a few examples:
- Image classification. This form of annotation trains your model to recognize the presence of similar objects based on similar collections of objects that it’s seen before. For example, a data annotator using image classification could tag a kitchen scene as “kitchen.”
- Object detection. Otherwise known as object recognition, this type of image annotation detects the presence, location, and number of certain objects in an image. For example, a street scene can be separately annotated with bikes, pedestrians, vehicles, and other objects.
- Segmentation. There are two main types of image segmentation. Semantic segmentation outlines the boundaries between similar objects (e.g., stadium vs. crowd) while instance segmentation labeling marks the occurrence of every individual object within an object class (e.g., every person in the crowd).
In addition to tools, there are a variety of image annotation techniques. Here are just a few of the most commonly used methods:
- Bounding box labeling. Annotators draw a box around target objects.
- Landmarking. Characteristics (such as facial features) within the image are “plotted.”
- Polygon labeling. Irregular objects are annotated by their edges.
What Do I Need to Get Started?
The right image annotation tool can help get the job done faster and with fewer errors using automatic image labeling. These are available on today’s market as open source or freeware image labeling tools.
If you’re working with an immense volume of data, you will need an experienced team of data annotators. Depending on the diversity of your datasets, more than one type of image annotation tool will be required.
Image annotation can often be a daunting task. Without the right tools, techniques, or workforce, you compromise on quality, precision, and the time it takes to get to market. That’s why AI companies often rely on professional data annotation services to label datasets for machine learning.
Professional Image Annotation Services
Machine learning models are only as good as the data that is used to train them. Keymakr has the skills, equipment, and expertise necessary to deliver pixel-perfect results that align with your timeframe and budget.
Are you interested in high-quality training datasets that have been labeled according to your standards and specifications? Get in touch with a team member to book your personalized demo today.