What is the Importance of Image Annotation in AI And Machine Learning?

Artificial Intelligence (AI) and Machine Learning (ML) are getting more and more interesting for computer engineers, who lightens-up this progressive technology implement into untapped fields or improve the performance and efficiency of the existing fields.

Availability of machine learning training data is a necessity for crucial point to improve AI performance. While image annotation is the technique used to create training data for visual perception model developed on the principles of AI and ML.

So, you need to first understand the importance of image annotation in AI and ML, so that you can further explore untouched fields where AI is needed. To make machines perceive objects in their natural surroundings you need annotated images that you can use to train the ML algorithm to learn and predict.

Detect Objects of Interest in Image

In machine learning or AI, you need to train the machine and detect the various types of objects visible in the natural environment. Self-driving cars, robots and autonomous flying machines cannot detect such objects unless trained with a certain process. Annotated images makes object of interest detectable to machines.


Bounding box image annotation is the technique, used to make different types of objects recognizable to machines through computer vision. It can be used to develop AI-enabled models for automotive, retail, and various other fields.

Classify the Varied Objects in Image

Object detection is not only the objective of image annotation, instead while classifying the objects, it works in a similar manner. Actually, there could be different types of objects in an image, and it becomes impossible for a machine to classify them.


For example, there is a dog and man in the same image, so both must be classified as different objects by recognizing the similar objects when shown to AI models in the real-life use. So, image annotation is the method used to classify such objects. However, the image annotation is used for computer vision to train the AI models.

Also Read: What Is Computer Vision: How It Works in Machine Learning and AI

Recognize Different Class of Objects

Similarly, when there are different types of objects in a single image, it becomes difficult to recognize them, because of similar dimension. In such cases object recognition and classification both are required at ground level.


And semantic segmentation is the most suitable technique that helps to classify the objects in a single class making easier for machines to differentiate between various types of objects. For computer vision, by using this technique objects can be configured with nested classifications, which is also very handy for similar properties.

Also Read: How To Label Data For Semantic Segmentation Deep Learning Models

Supervised Machine Learning Training

Another importance of image annotation is that it helps to create a label data sets for machine learning and AI. For supervised machine learning, annotated images must help algorithms easily and detect-cum-classify these objects.

In supervised machine learning, two types of algorithms are used. First classification, which helps to classify it into a desired category. Second, regression that helps to predict a value based on past data.

Used for Model Validation Training Data

Another most important objective of image annotation is while developing and AI or ML model it helps to validate the models to test for its accurate prediction. Annotated images are used to check whether model can detect, recognize, and classify the objects precisely and predict the same with accuracy.

In this process, machine learning model is validated by the experienced annotators and engineers. If annotated images will be not available, model won’t be able to detect the objects, and this process also helps to evaluate the quality of data labeling services. As, if images are not annotated properly, the algorithm will not be able to relate the same from its database or past experienced gained from machine learning.

Also Read: How to Validate Machine Learning Models: ML Model Validation Methods

Hence, image annotation has significant role in machine learning and AI development. But the quality of machine learning training data is the another aspect, should be considered to ensure your model is getting the right training. As, incorrect annotated images will misguide the machine due to inaccurate feed of data into algorithm.

Types of Image Annotation: Video

Cogito is one of the best image annotation companies providing high-quality training data for machine learning and AI developments. It is offering data and image annotation services for wide-ranging industries with all the leading techniques, making each object recognizable to machines with pixel-wise annotation at the lowest cost.