Suppose you want to train a security camera to detect crime activity or suspicious behavior. The more independent images you have, the more diverse and representative of the surrounding conditions they are, the better for you. You need hundreds, if not upwards of thousands of images to train a machine learning algorithm that makes fairly accurate predictions. However, diverse images, trained annotators, and a suitable annotation platform are the building blocks of every successful annotation project. The use cases are countless, and we'll get back to them shortly, but first things first: What is it that you need to know before starting your annotation project? What do you need to annotate images?ĭifferent image annotation projects may have slightly different requirements. The resulting data can help you train a model that can recognize and detect vehicles and discriminate them from pedestrians, traffic lights, or potential obstacles on the road to navigate safely.Īutonomous driving is one example of how image annotation fuels computer vision. The resulting data also referred to as structured data, is then fed to a machine learning algorithm, which is often understood as training a model.įor example, you can ask your annotators to annotate vehicles in a given set of images. It often involves human annotators using an image annotation tool to label images or tag relevant information, for example, by assigning relevant classes to different entities in an image. Image annotation is the practice of labeling images to train AI and machine learning models. How are companies doing image annotation?.What are some image annotation techniques?.What are the different types of image annotation?.Here are the questions this blog post will be covering: This blog post covers all you need to know about image annotation to make informed decisions for your business.

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