Image Annotation In Machine Learning A Brief Guide


Image annotation is a crucial action when creating machine learning models for object detection, categorisation, and segmentation tasks. Image annotation is a subset of data labelling practices comprehended by the representation of image tagging, transcribing, or digital labelling that involves labour-intensive human effort tagging images with metadata and associated attributes that will allow machines to identify objects competently.

So What Does It Mean To Annotate An Image?

Image annotation is characterised as the assignment of explicating an image with digital labels, generally involving human effort and, in some cases, computer-assisted assistance. Digital labels are predetermined by a machine learning systems engineer and are selected to provide the computer image model with extra knowledge about precisely what is shown. The process of digitally labelling images also helps machine learning systems engineers hone in on important factors that determine their model’s overall exactness and precision. Examples include potential naming and categorisation problems, representing obstructed objects, explicitly identifying with those unrecognisable image sections, etc.

How Do You Annotate An Image?

An engineer can apply a series of digital labels from an image by bounding boxes to the pertinent objects, thereby digitally annotating the image. For example, for an image representing a busy street, pedestrians can be marked in a specific colour and trucks (for example) marked in another colour. This process is repeated until all required images are digitally annotated – and depending on the project’s use case, the quantity of digital labels per image may vary. For example, some business projects will need only one digital label to represent the pertinent content of an image.

How Can a Data Platform Support Complex Image Annotation?

Image annotation business projects commence by identifying and familiarising annotators with the right tools to perform the annotation undertakings. Digital annotators need to be comprehensively trained on each annotation project’s technical specifications and governing guidelines, as every business will have distinct business needs and requirements.

Once the digital annotators become qualified on the data annotation facet, they can commence annotating multiple images on a data training platform reserved solely for digital image annotation. A data training platform is a software environment that has been designed to maintain all the necessary technical tools for any desired digital annotation exercise.

The success of any computer vision activity will always rely on image annotation – the essential automated data labelling process behind this technology makes computer systems bear intelligent and definitive decisions. There cannot be any competent computer vision processing without the complementary image annotation.

Key Challenges in the Image Annotation Process Required for Machine Learning

Following are a few of the key challenges that any Artificial Intelligent (AI) workforce will encounter:

Automated Annotation vs Human Annotation: 

The cost of the digital data annotation process chosen by the business will depend on the approach employed. Digital annotation operating with computerised tools can promise a certain level of accuracy – and consistently too. However, human annotation practices will take time and can be expensive due to the skill set required and the availability of such resources. Furthermore, accuracy is more prevalent using this method in the short term.

Assured High-Quality Digital Data Outcomes: 

High-quality data will provide the best outcome for any ML business model, and some may say that is the key challenge in itself. An ML business model can only construct accurate projections if the data quality is adequate and uniform. Subjective data is challenging to interpret for digital data labellers depending on where they are geographically located globally.

Identifying and Selecting the Most Suitable Annotation Tool: 

Constructing high-quality digital datasets requires combining the proper digital data annotation tools and a highly skilled labour workforce. In addition, diverse data types are used for digital data labelling, and understanding what key aspects to contemplate when choosing the right annotation tool is a must for any enterprise planning to embark on any ML data journey.

Digital image annotation is a vital component of any AI technical development exercise, and it is one of the fundamental tasks required for any computer vision technology. Digitally annotated images are a must to effectively teach ML algorithms to ‘recognise’ specific entities comprised within digital visuals and thereby provide computers with the ability not just to look – but ‘see’ – similar entities, just as we humans can do.


Those enterprises that have welcomed complex ML business models with determination comprehend that high-quality digital data is all that counts. While there are distinguishable types of image annotation tools on the market, the process of digitally labelling any image(s) comes with a horde of challenges. Nonetheless, the key challenge is making sure ML business models serve at their ideal level after completing the digital annotation process.