Have you ever wondered what makes an artificial intelligence (AI) data processing project succeed—or even fail? Simply, these are data annotation tools that data annotators use in improving their datasets for training and deploying machine learning (ML) models. These tools serve as a key factor whether an AI firm can build a high-quality model that fuels a disruptive solution and solves complex problems, or whether they’ll simply waste time and money on a failed experiment.
In this article, we’ll discuss what a data annotation tool is and its important features. Plus, we’ll share with you the factors you should consider when choosing data annotation tool. Let’s dig in.
What is a Data Annotation Tool?
To define, data annotation tool is a cloud-based, on-premise, or containerized software solution, platform, or portal that data scientists use to annotate, tag, or label all types of high-quality training datasets for ML models. It is generally designed to handle specific data types, which include image, video, text, audio, and sensor, which serves as a driving force that could influence an AI project.
Data annotation tools can be either purchased and leased or built, depending on how an organization would want to manage their datasets and requirements regarding customization security and frequency. While there are companies outsourcing data annotation services, there are also firms that use their own tools which are custom-built and are based on freeware or opensource tools available in the market.
Key Features of Data Annotation Tools
Data annotation tools are integral throughout the whole annotation process. Aside from accelerating the speed and increasing production quality, these tools also help businesses and companies with management and security. Read on to know the important features of data annotation tools.
1. Dataset Management
Regardless of the tool you prefer to use, annotation always starts and ends with a broad way of handling the datasets you wish to annotate. Hence, it’s crucial that you guarantee the tool you are considering will actually import, export, and support the high volume of data and file types you need to label. This involves searching, sorting, filtering, cloning, and merging of datasets.
2. Annotation Methods
The methods and capabilities in applying labels to your data are the core features of data annotation tools. However, take note that not all tools are created equal when it comes to this. While there are tools that are narrowly optimized to focus on specific types of labeling, there are also tools that enables different types of use cases.
Some of the common types of annotation capabilities offered by data annotation tools include creating and managing ontologies or guidelines, such as classes, attributes, and labels maps. Below are just a few samples:
- Image or video: Bounding boxes, polygons, polylines, classification, 2-D and 3-D points, segmentation, tracking, interpolation, or transcription;
- Text: Transcription, sentiment analysis, net entity relationships (NER), parts of speech (POS), dependency resolution, or coreference resolution; and
- Audio: Audio labeling, audio to text, tagging, and time labeling.
3. Data Quality Control
Your ML and AI models’ performance will only be as good as your data, and annotation tools are integral in managing the quality control (QC) and verification process. Preferably, the tool will integrate QC as part of the annotation process.
To cite an example, providing real-time feedback and executing problem tracking during annotation is crucial. Moreover, these can help with different workflow procedures such as labeling agreements. A number of tools will offer a quality dashboard to assist managers in viewing and monitoring quality issues, as well as in delegating QC tasks to the core annotation team or to a specialized QC team.
4. Workforce Management
Although the process of annotation involves AI, all types of data annotation tools must be used by a human workforce. Hence, you still need humans who will handle exceptions and quality assurance. Nonetheless, some leading tools offer workforce management features, such as tasks assignment and productivity analytics to measure the time spent on each task.
Keeping your data free from cyber threats and breach should be your utmost priority during the annotation process. That being said; it’s important that tools used must restrict an annotator’s viewing rights to prevent data downloads, whether annotating sensitive protected personal information (PPI) or your own valuable intellectual property (IP). Furthermore, regardless of how the tool is deployed, a data annotation tool should provide secure file access.
Factors to Consider in Choosing a Data Annotation Tool
Since there are a variety of data annotation tools available in the market, you have to be very careful in choosing one that can make your project a success. Below are some of the factors you should consider when selecting the best data annotation tool.
Because annotations are manual by nature, image labeling may require a considerable period of time and resources. Therefore, you should look for data annotation tools that can complete manual annotation as fast as possible. Check its user interface (UI), hotkey support, and features that will help you save time and boost annotation quality.
In computer vision, labels vary depending on the project you’re dealing with. To cite a few examples, in classification, single label is needed that describes a class for a given image. For object detection, which is a more complex task in computer vision, it requires a class label for each object. As for bounding box, you need a set of coordinates that clearly specifies where an object is positioned within an image. Moreover, both class label and a pixel-level mask within an object outline are required for semantic segmentation.
With that, it’s important to have an annotation tool that has all of the features you need to help you with your project.
Annotations are available in a variety of formats, including COCO JSONs, Pascal VOC XMLs, TFRecords, text files, and picture masks. While it’s true that we can convert annotations from one format to another whenever we want, having a data annotation tool that directly churns out annotation in your preferred format can help improve your data preparation routine and save a lot of time.
In need of a web-based annotation application? Or when working on annotations, do you prefer a window app that can be used both online and offline? When looking for tools, always remember that some tools are compatible with both desktop and web-based applications. On the other hand, there are tools that can only handle web-based annotation, in which you won’t be able to use except in a web browser window.
Cost is one of the most common deciding factors when choosing what tool to use on your annotation. If you find the best annotation tool with all the functionality and flexibility that you need, it is surely too expensive. Meanwhile, an open-source, web-based, and free-to-use annotation tool can help you save cost; however, features of such tools might be lacking.
Best Data Annotation Tools
Now that you have an idea on what factors you should consider when choosing annotation tools, we’ll also give you a rundown of the best data annotation tools available in the market.
1. Commercial Data Annotation Tools
If your firm is at the growth or enterprise stage, commercial data annotation tool is your top choice as it offers full-featured, complete-workflow commercial tools for data labeling. It’s also a wise decision to purchase tools that are commercially available and modify them with few development resources of your own if you wish to sustain that growth over the years.
Additionally, purchasing an existing, enterprise-ready, and tested data annotation tool can help expedite your project timeline. Some of commercial data annotation tools include Annotell, Dataloop AI, Datasaur AI, Deepen AI, Hasty, Hivemind, LightTag, UnderstandAI, and V7 Labs Darwin.
2. Opensource Data Annotation Tools
These tools allow you to use or change the source code, customize a number of features to meet your needs, and take control over integration. Opensource data annotation tools can also give more flexibility as your tasks and data operations progress over time. Users of these tools are part of a collaborative community who can share use cases, best practices, and feature improvements made by changing the original source code.
Despite these good points, a few barriers to scale and production are expected when using such tools as they are typically built for a single user, including poor workflow or workforce management. CVAT, Fiji, Labellmg, LabelMe, and VoTT are examples of opensource annotation tools.
3. Freeware Data Annotation Tools
Without bringing money out of your pocket, you can download, install, use, and share freeware data annotation tools. Similar with opensource data annotation tools, freeware tools are also streamlined by the members of the community. These tools can also be beneficial when you have development resources and you plan to build your own data annotation tool. An example of freeware is Colabeler.
Let Our Specialists Meet Your Data Annotation Needs
As stated above, the success of a company’s AI data processing projects rely on the data annotation tools they’ll be using. However, not all firms have their own resources to work on their AI projects, especially start-ups and small-sized businesses. But fret no more! We’re here to help.
At Outsource-Philippines, we take pride in housing highly skilled data annotators whom you can work with to achieve project success. Our team of experts are capable of providing wide-ranging and secured data solutions to clients in diverse fields, thereby addressing their ML challenges. Plus, you don’t have to spend more money as our data annotation services are just within your means!
Ready to get started? Contact us today.