In machine learning, having the right data on hand is the most fundamental and, arguably, the only way to succeed in the data-driven industry. But what is the right data? In the most general sense, it’s the one that has been processed and prepared for the model training.
Such data is most commonly known as labeled data. It tells the machine or any other sophisticated computer system what a dataset is about and trains it to recognize any kind of information that dataset contains. Data annotation is, therefore, a crucial part of any project in AI. It’s the process of putting meaningful labels on pieces of data (i.e., images, videos, audio files, or text files). This way, the data becomes understandable for ML models, and they can learn from it to achieve high performance and deliver trustworthy results.
For instance, a computer vision model used to develop a self-driving vehicle requires a considerable amount of labeled data, such as images of the roads, pedestrians, road signs and obstacles, etc. The accuracy of these annotations directly affects the accuracy of the driverless system, as well as the reliability and safety of the vehicle itself. This is why data annotation must be carried out by professionals only.
However, there are a plethora of tools and platforms to help businesses or individual clients handle both simple and intricate data labeling tasks. It’s a great alternative to hiring your own in-house team or looking for an outsourcing partner. Let’s see what these tool options are!
Data Annotation Tools; Why Should Businesses care?
Different tools used in data labeling serve as a great example of how automation, or even semi-automation, adds to the tedious process of preparing data for model training and building complex AI systems. A data annotation tool is a piece of software that can locate unlabeled (aka raw and unstructured) data in the image, video, text, or audio file.
Businesses may use a range of tools integrated into data labeling software to transform raw data into a meaningful source of information, which helps them create high-performing AI algorithms. How does a data annotation platform work?
- The user enters a dataset using the labeling tool of choice.
- The software assigns a relevant tag (aka label) using machine learning, a human workforce, or the users themselves.
- Some platforms enable the combination of the three, allowing the user to select the annotator depending on aspects like cost, quality, and speed.
Data labeling technologies vary in terms of the type of data that needs to be labeled, such as image, video, audio, and text. Also, they differ in terms of where this data is being used (e.g., satellite imaging, LIDAR, etc.). Most annotation tools employ criteria, including consensus, ground truth, and others, to rate the quality of the performed labeling task.
This way, data labeling tools and platforms are programmed to use certain methods like bounding boxes, landmark annotation, and polylines, to name a few, to assist data experts and AI companies in preparing their data for machine learning.
Check out a detailed overview of the most popular annotation tools to get more information on the topic. Or keep scrolling!
Data Labeling Tools & Platforms: Selection of the Finest
- Amazon
A state-of-the-art automated solution for data annotation known as Amazon SageMaker Ground Truth offers fully managed services. They vary from simple labeling tasks to working with 2D and 3D data, as well as auto-segmentation tools. A great option to reduce the time of annotating an ML dataset.
- Appen
An innovative AI-assisted data labeling platform that provides high-quality labeled training data for specific machine learning projects. The mechanism behind this advanced platform is based on the mix of human intelligence and cutting-edge AI models, allowing it to easily annotate all types of data and provide the precise ground truth the models require.
- Label Studio
A web-based platform that provides services for labeling data and exploring different types of data. The annotated datasets are generally accurate and suitable for various machine learning applications. Label Studio platform can be used from any browser.
- LightTag
This tool is designed for NLP (natural language processing) tasks that require text data to be labeled. LightTag is created for the collaborative work between the ML teams. It has a simple user interface that lets its users easily organize the process and optimize the data labeling tasks. What’s more, this platform offers quality control tools for the most efficient results.
- SuperAnnotate
A full-featured approach to data annotation for computer vision systems. This platform offers a whole framework for automating, labeling, and training CV models as it works with data in audio, text, image, or video formats, as well as LiDAR data. High model performance and accurate data annotation are achieved through useful features, automated predictions, and quality control.
- CVAT
Short for computer vision annotation tool, CVAT provides a broad variety of functions for the purpose of labeling computer vision data. The tool enables tasks like image segmentation, image classification, and object identification. However, it might be hard to adapt to the user interface, which is one of the biggest downsides. Plus, the application only works with Google Chrome.
- Labelbox
The data labeling tool that gives comprehensive visibility and control over every step of the labeling activities. In Labelbox, advanced pre-labeling techniques are integrated with reliable automated methods. Their top-tier labeling partners speak more than 20 languages fluently and have backgrounds in the life sciences, fashion, medicine, and agriculture.
- Doccano
An open-source data annotation software developed for machine learning practitioners. It has capabilities for sequence labeling, sequence to sequence comparison, and text categorization. Doccano allows labeling data for sentiment analysis, named entity recognition, text summarizing, etc.
- Supervisely
A potent data labeling platform for computer vision that allows its users to annotate datasets and neural networks. Its video labeling tool has modern, class-neutral neural networks that are great for object tracking. Supervisely deals with the labeling of images, videos, 3D point clouds, volumetric slices, and other data types. It also allows you to keep track of productivity and annotation performance.
- Dataloop
This platform makes it possible to handle unstructured data, including images, audio files, and videos, and label it using a variety of tools (e.g., box, polygon, classification, etc.). The quality assurance process is included as well, since the annotation work is accomplished in tasks, annotation tasks, or QA tasks. Users may run their own or open-source software as services on different sorts of compute nodes using Dataloop’s automation.
- Sloth
An open-source platform that offers flexible capabilities for computer vision data annotation. With Sloth, one can label the data using either custom settings or existing presets. It’s an easy-to-use tool where the entire process is under the user’s control, including installation, labeling, and the creation of properly referenced visualization datasets.
- Tagtog
A text annotation tool for NLP tasks, which helps create customized datasets for text-based AI. The labeling procedure is adapted for various text types and activities. Additionally, Tagtog provides a framework for controlling the manual text labeling procedure, including ML models to speed up the procedure. The software allows team collaboration, secure cloud storage, ML and dictionary annotations, multiple languages, different file formats, and QA.
- Lionbridge AI
A complete platform for data annotation that provides unique datasets for the biggest tech companies in the world. Also, it’s a quick and cost-effective way to create unique training datasets while maintaining the accuracy of the data. The application has special functionality for managing text, audio, picture, and video data and supports all popular file types.
On a Final Note: Tools vs. Annotators?
Given the abundance of data labeling tools available today, the role of human annotators is still not diminished. Why is that so, and which option is best for modern businesses?
Data annotation used to be a laborious process that was heavily reliant on AI and ML. Today, however, such annotation services may completely remove human mistakes through automation, intelligent prediction, and collaborative workflows thanks to recent advances in the tech sector.
The correct quantity and quality of annotated datasets are necessary for the development of an accurate and completely functioning model. Human annotators are seen to do the job better in terms of subjectivity, managing, and dealing with ambiguity. Yet, they need annotation tools to automate and speed up the process, as well as keep the human error rate under control.
So we say it’s all about the skilled combination of both human supervision and automation capabilities of today’s AI-powered systems.
