In the world of AI, data is king. And while data annotation may be the least glamorous aspect of machine learning, it’s also one of the most important. That’s because the performance of your algorithms hinges directly on the quality of the data you use to train them.
High-quality data annotation begins with finding the right tool to annotate your data. But this isn’t always easy. As machine learning expands across industries, an increasing number of data annotation tools are entering the marketplace. Improvements to existing tools, moreover, take place monthly.
Are you having trouble finding the right image annotation tool for your computer vision project? Create high-quality image datasets for machine learning with our comprehensive guide on navigating an increasingly competitive marketplace for data annotation tools.
What Is Data Annotation?
Let’s start with the basics. What is data annotation? When it comes to machine learning, data annotation refers to the process of labeling training data, which is then used to “train” a set of algorithms to detect, recognize, and interpret objects of interest in the machine learning model’s environment.
The process of annotating data isn’t always the same. That’s because data comes in many forms. Data annotation may involve labeling, tagging, processing, or transcribing. Image classification data, for example, involves outlining significant objects within images. Once a computer vision model is trained and deployed on image classification data, it learns to recognize these objects and perform an action or make a decision as a result.
What Is an Image Annotation Tool and What Does It Do?
Image annotation tools are used to streamline the process of image annotation. Some AI companies choose to build their own tools while others take advantage of those that are available on the marketplace.
A web image annotation tool can be made available through open-source or freeware. Tools may be cloud-based, on-premise, or sold as individual software solutions.
Factors to Keep in Mind When Choosing an Image Annotation Tool
1. Dataset Management
The purpose of data annotation is to label your data. Make sure your image annotation tool can handle the volume and type of data you intend to annotate. Dataset functions such as search, clone, filter, and merge should be fully supported to ease your workflow.
File storage is another essential factor to keep an eye on. Where will you store annotated data? Always check whether a tool supports local, network, or cloud storage, and what limitations may be in place.
Much of the buzz in the data annotation community has revolved around automation, otherwise known as auto-labeling. Automation comes in many forms, from converting bounding boxes into polygons to removing the need for any human intervention whatsoever. But there’s a catch.
While some annotation tasks—such as pre-annotation—are easily automated, others suffer in quality without a skilled annotator pulling the strings. A human-in-the-loop approach is always necessary for quality assurance.
3. Quality Control
Any reliable data annotation tool has built-in quality control processes. Given that the performance of a machine learning project rests on the quality of its training data, striving for pixel-perfect results pays off.
Processes such as checking for labeling consensus, garnering real-time feedback, and tracking issues are all central to ensuring that the quality of your training data meets your standards. That’s why AI companies often assign a specialized quality assurance team alongside a team of core annotators.
4. Data Security
In the digital age we live in, data security has become increasingly important. Whether you’re annotating sensitive protected data or your own valuable intellectual property, keeping that data secure should be one of your top priorities.
To keep your data from falling into the wrong hands, make sure your data annotation tool offers secure file access. Set up barriers to access, such as restricted viewing rights depending on your level of authorization.
Keymakr: High-Quality Image Annotation That Scales
Choosing the right data annotation tool for your machine learning project takes careful research, especially because tooling features are growing more complex by the day. Depending on the nature and scope of your machine learning application, it may benefit you to rely on professional data annotation services.
AI companies often choose to outsource image annotation to produce high-quality training datasets in a short period of time. Keymakr delivers the pixel-perfect results that your algorithms crave. Our image annotation solutions include a variety of cutting-edge tools that are uniquely geared toward the type of data we’re working with.
Get to market faster with Keymakr. Contact a team member to book your personalized demo today.