How To Choose The Right Image Annotation Tool

How To Choose The Right Image Annotation Tool

Image annotation has become a crucial aspect of machine learning as the world advances into the age of artificial intelligence. A simple definition of image annotation in machine learning is the process by which human annotators label objects to help machines recognize the said objects in the given image. The annotator essentially adds metadata to existing data sets for the machine-learning algorithm to interpret.

According to a 2018 report(1), the market for data annotation is valued at $500 million—and this figure is expected to soar in the coming years. However, advancements in this industry also lead to more options as every machine learning, and artificial intelligence model that performs computer vision tasks requires image annotation tools that cater to their specific data set needs.

The right image annotation tool will help optimize your data annotation process and ensure the success of your project. The following criteria will help you select the best tool for the job:

1. Project needs assessment

The first step is to identify the needs of the overall job/project. These needs usually stem from the desired outcome of the project. This phase entails understanding the following:

  • Project size
  • The time required for the project
  • Your technical capabilities
  • The budget you have for the project

The needs assessment phase will help determine whether it would be wise to perform the task independently or outsource image annotation services. It is, therefore, necessary to analyze the quality of output desired from the project to understand the simplicity or complexity of the tasks. Accomplishing this phase will also help set the pace of the project workflow.

Image Annotation Tool

2. Functionality

The needs assessment will help you develop a checklist of the features desired from the annotation tool. Easy-to-use options that possess intuitive shortcuts and easy on-premises installation are vital features to consider when determining the degree of user-friendliness of the tool.

It is equally important to know whether you want a standalone or web browser annotation tool. Standalone tools offer more data security and function based on the capabilities of your machine. They’re also fast applications with a broader range of functions.

On the other hand, web browser annotation tools are easy to deploy and can be used remotely on machines with different system specifications. They’re suitable for monitoring progress remotely.

3.     Core design

The core designs of various tools are suited for different project needs. For example, the annotation tools available today are based on artificial intelligence techniques and auto-labelling features. These features aid human annotators in bettering their annotations and can be programmed to perform the task automatically.

Some tools possess integration capabilities. They provide you with a plug-in option to integrate your machine-learning algorithm. Finally, it is crucial to assess the tool’s data storage and processing capabilities and the image formats it supports. This feature should allow you to manipulate the data to the specifications of your project.

Image Annotation Tool

4. Quality control

The ideal annotation tool should possess quality control mechanisms or have the option to allow the user to input their quality control standards. This feature will not only help produce high-quality annotations but also in the distribution of tasks to a team to maximize quality output. After all, as a recent study found, supervised machine learning models are better than unsupervised ones.

The quality control mechanisms in question may include reviewing, grading, flagging errors and assigning tasks based on the competence of an annotator. The tool should be able to review the annotations multiple times to ensure they’re accurate.

The review will provide a grading criterion for each annotation, allowing project managers to monitor remote annotators in real-time. The tool will automatically flag any errors spotted during the annotation process. The manager can also train annotators to avoid flagged mistakes in the future.

5. Budget allocation

The final criterion for selecting a tool is your budget. The size of the team and frequency of usage of the tool will determine how much you’ll spend. Open-source and freeware tools are recommended for one-time use and small annotation projects. They’re low-cost and ideal for projects with preloaded image data.

Open-source and freeware tools, however, have a few drawbacks, such as inadequate monitoring of remote annotators and requiring extra development resources to manipulate the tool to project needs.

The case is different for commercial solutions. They are ideal for upscaling projects and hastening timelines. Some commercial solutions offer flat rates for one-time usage with access to all the tool features.


The right image annotation technology offers collaboration between business experts, external workforce, and data scientists on projects as users. The selection criteria listed above will help you select the imaging annotation tool that best suits your needs. Give it a try to narrow your search for the right tool for your project.


Categories: Technology

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