Generating labeled training data requires a great deal of time, effort, and investment. If you’re building a machine learning model, chances are you’re going to need data labeling tools to quickly put together datasets and ensure high-quality data production.
The best data labeling tools are simple to use, minimize human involvement, and maximize efficiency while keeping quality consistent. In this article, we present the eight best annotation tools to help you create training datasets for machine learning.
Tips for Selecting a Data Labeling Tool
Data labeling tools vary in the features they offer, file types they support, data security practices, storage options, and more. Here are a few things to look for when evaluating data labeling tools:
- An intuitive user experience
- APIs, or an easy way to connect the tool to private APIs
- Advanced project management features
- A wide range of capabilities and supported file types
- Automation tools to boost labeling efficiency
That said, the right tool for you will depend on your project’s scope, scale, budget and timeline. To help you find the perfect tool, below we will introduce eight of the best data labeling tools for machine learning.
Top Data Labeling Tools for Machine Learning
Lionbridge AI offers an end-to-end data labeling and annotation platform for data scientists looking to train machine learning models. With over 20 years of hands-on experience creating custom data for the world’s largest technology companies, Lionbridge AI has built the most intuitive data annotation platform on the market.
This all-in-one platform allows you to build custom training datasets quickly and cost effectively while maintaining data quality. Furthermore, the tool works for all major file types, with unique features to handle text, audio, image & video data.
The platform gives you maximum control and flexibility to customize your task, workflow and quality checks. Furthermore, you’re also given the option to invite your own annotators onto the platform, or hire from Lionbridge’s network of over 500,000 qualified contributors.
Also known as MTurk, Amazon Mechanical Turk is a popular crowdsourcing marketplace commonly used for data labeling. As a requester on Amazon Mechanical Turk, you can design, publish, and coordinate a wide range of human intelligence tasks (known as HITs), such as text classification, transcriptions, or surveys. The MTurk platform provides useful tools to describe your task, specify consensus rules, and define the amount you’re willing to spend for each item.
Although it is known to be one of the cheapest data labeling tools on the market, there are several drawbacks to using the MTurk platform. For one, it lacks key quality control features. Unlike companies like LionbridgeAI, MTurk offers very little in the way of quality assurance, worker testing, or detailed reporting. Furthermore, MTurk places a heavy project management burden on requesters to design tasks and recruit workers themselves.
The Computer Vision Annotation Tool (CVAT) is a web-based tool for annotating digital images and videos. The tool supports tasks like object detection, image segmentation and image classification. Although the tool itself requires some time to learn and master, CVAT boasts a wide range of features for labeling computer vision data.
However, there are a few drawbacks to using CVAT. For one, the user interface is quite complicated, and can take several days to get used to. Not only this, but the tool only works in Google Chrome. It has not been tested in other browsers, making it difficult to conduct large scale projects with multiple annotators. Furthermore, all quality checks need to be done manually, which can slow the development testing.
SuperAnnotate is a data annotation platform for image, video, LiDar, text, and audio data. Using the more advanced features of their platform, such as automatic predictions, transfer learning, and data and quality management, they claim that their platform can speed up annotation tasks by at least three times.
LightTag is a tool for businesses and researchers to label text data in-house. While the starter package is free, each membership tier increases in cost and has a monthly maximum number of annotations, starting from 1,000 annotations a month.
Founded in 2018, DataTurks is a relatively new startup that provides services for labeling text, image, and video data. Although the labeling platform is open source and free to use, DataTurks seems to have stopped working on its product following their acquisition by Walmart earlier this year.
Playment is an image annotation company that you can use to build training datasets for computer vision models. For example, a few of the services offered include bounding boxes, cuboids, points and lines, polygons, semantic segmentation, and object recognition.
Based in Poland, Tagtog is a text labeling tool that can be used to annotate data both automatically or manually. Aside from the TagTog tool itself, the company also has a network of expert workers from various fields that can annotate specialized texts.
LabelBox is a collaborative training data tool for machine learning teams. The platform provides one place for data labeling, data management, and data science tasks. A few of LabelBox’s features include bounding box image annotation, text classification, and more.
If you’re looking for a quick and easy data labeling tool, get in touch with Lionbridge AI. We make data labeling easy with our intuitive platform: simply upload data, add your team, and build custom datasets in hours. In addition to our data labeling platform, Lionbridge AI unlocks access to 500,000 qualified annotators that can quickly and precisely label datasets.