Giving eyes to your machine is no longer a dream, thanks to artificial intelligence technologies a machine can perceive the objects constituting the environment in which it is located, i.e. analyze, process and understand several images, this is called computer vision.
The ability to see and interpret the world can provide us with seemingly cutting-edge technology such as medical imaging analysis, optical character recognition, and facial and gesture recognition.
However, this will not be possible without using clear, annotated data to form machine learning models.
Indeed, the performance rate of machine learning and deep learning algorithms* depends on the accuracy rate of the training data on which these models are based.
The machine learning models are fed from the data provided. As soon as an algorithm has processed enough annotated data, it can begin to automatically recognize recurring new non-annotated data in the annotated data provided to it.
In other words, annotation is a manual task that involves assigning labels or metadata to a dataset, it is also a type of data labeling.
Image annotation is used to identify objects and to segment images, it marks the data that the machine learning system is supposed to recognize, it is an indispensable step in the pre-processing of data in supervised learning .