Artificial Intelligence (AI) is advancing rapidly, and the success of AI projects largely depends on the training data. In order to enrich the data for training and deploying machine learning (ML) models, engineers use data annotation tools.
The choice of these data annotation tools can lead to a high-performing model that results in a disruptive solution. It could also result in a failed AI program that ends up being a waste of time and resources.
Data Annotation Tool: what is it and types
In machine learning, data annotation refers to the process of labelling data to show the outcome you want your machine learning model to predict. This process sees labelling, tagging, transcribing, or processing a dataset with features you want your ML to recognise as an outcome.
While the data annotation tool is of extreme importance, choosing the tool is not a fast or easy decision. These tools are advancing on a monthly basis, if not by the week. The changes are often released in the form of an improvement to existing tools and new tools for emerging use cases.
The best data annotation tools available right now are classified in three main categories: open source, closed source, and outsourcing.
Here is a look at five of the most popular data annotation tools available right now.
Diffgram
Diffgram is widely considered to be the best open source data annotation tool that supports most integrations and has the most flexible deployment option. It also runs anywhere and the way you want. As a data annotation tool, Diffgram also imposes the least limits and scales out of the box to large, multi-user setups.
It also offers deep support for images and videos, and as a complete system, it includes features such as team collaboration and large scale use cases. Diffgram is best used for semantic segmentation and for images and video annotation.
Label Studio
Label Studio is best for those looking for a configurable interface. It supports all types of data and many types of annotations as well. While the broad nature of data support is commendable, the depth is not that vast and some machine learning engineers might find it lacking in terms of use case.
As an open source software, Label Studio is also among the most flexible data annotation tools and is quickly installable. The pre-built templates make for a good start while the option to build custom UIs make Label Studio a good option for those looking to do their labelling in a multi-domain environment.
Intel CVAT (Computer Vision Annotation Tool)
Intel CVAT is another open source data annotation tool funded by Intel. With CVAT, Intel is attempting to build a data annotation tool around its OpenVino offering and in a way, the chipmaker is trying to sell its ML and AI hardware.
With CVAT, you are reassured that the tool is maintained by some of the smartest people in the industry but at the same time, the tool is limited to a hardware giant with vested interest in the space. If you are a full time annotator, CVAT makes for a great option and is not aimed at first time users.
LabelBox
LabelBox is a closed source data annotation tool that claims to support full natural language processing. Based in San Francisco and founded in 2017, the tool offers support for images and even has a video interface that has come out of beta now.
However, LabelBox has its own caveats including the fact that its login system is fully outsourced and some users have criticised the downtime. LabelBox was also one of the early movers in the data annotation space but it has kind of scaled back its ambitions.
V7 Labs
V7 Labs is another closed source data annotation tool that is offered as a medical focussed tool. It supports 2D, volumetric 3D, video, container and SaaS annotation. The V7 Labs Darwin is best for auto-annotation and quality review.
As the winner of 2021 CogX award for Best AI product in Healthcare, this commercially available tool offers an user experience crafted for professionals, bundling built-in automation, and support for every tool and image data format.