Why do artificial intelligence companies spend so much time creating and refining training datasets for machine learning projects? The answer is simple. All else being equal, the higher the quality of your training datasets, the better your machine learning application will perform.
But what determines the quality of a training dataset? That’s where data annotation comes in. What is data annotation? Why are human-annotated datasets significant when it comes to machine learning performance?
Whether we’re talking about product recommendations and search engine results, or self-driving cars and autonomous drones, high-quality, human-powered data annotation helps build and improve machine learning applications across industries.
What Is Data Annotation?
Let’s begin with the basics. For a machine learning model to understand what it’s looking for in a cluttered, real-world environment, it needs to draw from experience. This comes in the form of training data.
Teaching a machine learning model to interpret its environment, make decisions, and take action requires immense volumes of high-quality training data. The process of creating training data involves accurate categorization for specific use cases. This is known as data annotation.
A data annotator’s job is to show the machine learning model what outcome to predict. In practice, data annotation is the process of transcribing, tagging, and labeling significant features within your data. These are the features that you want your machine learning system to recognize on its own, with real-world data that hasn’t been annotated.
What Is Image Annotation in AI?
When it comes to visual perception models like self-driving cars and facial recognition software, image annotation plays a vital role in allowing these models to interpret and interact with their surroundings—whether those surroundings include a busy street or the Earth as it’s observed from space.
Image annotation is useful for the production of computer vision datasets. The process involves assigning metadata to images in the form of captions, keywords, identifiers, and more. Different types of image annotation allow computer vision models to increase the accuracy and precision with which they interpret the world around them.
What Is Human-Annotated Data?
Machine learning models require both human and machine intelligence. This is called a human-in-the-loop model, where human judgment is used to continuously improve the performance of a machine learning model. Likewise, the process of data annotation needs humans.
Human-annotated data powers machine learning. When it comes to data annotation, human judgment introduces subjectivity, intent, and clarification. In some ambiguous cases, such as when determining whether a search result is relevant, more than one human is necessary to reach a consensus.
High-quality training data is the lifeblood of computer vision applications. Machine learning is dependent on the quality and quantity of its training data. The importance of datasets in machine learning can be summarized by one saying: “garbage in, garbage out.”
Professional Data Annotation Services for Computer Vision
To produce high-quality training datasets, artificial intelligence companies often rely on professional data annotation services to improve the performance of their machine learning model. These companies use a combination of software, verified workflows, and skilled annotators to produce, structure, and label high volumes of training and testing data.
Machine learning models are only as good as the data that is used to train them. Keymakr has a diverse array of tools, verified processes, and highly experienced data annotators to label your images according to your standards and specifications.
Get to market faster with Keymakr. We provide pixel-perfect image and video annotations that meet your deadlines and suit your budget. Get in touch with a team member to book your personalized demo today.