Top Data Labeling Tools For Machine Learning in 2023

What is data labeling?

Data labeling in machine learning is annotating unlabeled data (such as photos, text files, videos, etc.) and adding one or more insightful labels to give the data context so that a machine learning model may learn from it. Labels might say, for instance, if a photograph shows a bird or an automobile, which words were spoken in an audio recording, or whether a tumor is visible on an x-ray. Data labeling is necessary for many use cases, such as computer vision, natural language processing, and speech recognition.

Various machine learning and deep learning use cases, such as computer vision and natural language processing, are supported by data labeling.

How is data labeling implemented?

To clean, arrange, and label data, businesses incorporate software, procedures, and data annotators. These labels allow analysts to separate certain variables inside datasets, facilitating the choice of the best data predictors for ML models. The labels specify which data vectors should be used for model training, during which the model improves its ability to predict the future. Machine learning models are built on top of this training data.

Data labeling jobs require “human-in-the-loop (HITL)” engagement and machine support. HITL uses human “data labelers’” expertise to train, test, and improve machine learning models. By feeding the models the datasets that are most pertinent to a particular project, they aid in directing the data labeling process.

Comparing labeled and unlabeled data

• Unsupervised learning uses unlabeled data, whereas supervised learning uses labeled data.
• Unlabeled data is simpler to obtain and keep than labeled data, making it cheaper and more convenient.
• Unlabeled data has a more limited range of applications than labeled data to provide actionable insights (for example, predicting activities). Unsupervised learning techniques can aid in discovering fresh data clusters, enabling new labeling.
• To eliminate the requirement for manually labeled data while still delivering a sizable annotated dataset, computers can also use combined data for semi-supervised learning.

An essential step in creating a high-performance ML model is data labeling. Although labeling seems straightforward, it’s not always simple to use. As a result, businesses must weigh various aspects and strategies to choose the most Approaches to data labeling

effective labeling strategy. A thorough evaluation of the task complexity and the project’s size, scope, and duration is advised because each data labeling approach has advantages and disadvantages.

You can label your data in the following ways:

• Internal labeling: Using in-house data scientists makes monitoring more accessible and improves quality. This strategy, however, often takes more time and is more advantageous to big businesses with lots of resources.
• Synthetic labeling: This method improves the data quality and time efficiency and creates new project data from pre-existing datasets. Synthetic labeling, however, necessitates a lot of computational power, which might raise the cost.
• Programmatic labeling – This automated data labeling procedure uses scripts to save time and eliminate the need for human annotation. However, due to the likelihood of technical issues, HITL must continue to be involved in the quality assurance (QA) procedure.
• Crowdsourcing – This method, which allows for micro-tasking and web-based distribution, is speedier and more affordable. However, crowdsourcing platforms differ between project management, QA, and labor quality. Recaptcha is among the most well-known instances of crowdsourced data labeling. This project has two purposes: it improved image data annotation while preventing bots from being used. To demonstrate that they were human, a user may be asked to identify all the images that had cars in a Recaptcha prompt, and the program could then verify itself using the results of other users. These users’ contributions helped create a database of labels for various photos.

Best Tools for Data Labeling

Amazon SageMaker Ground Truth

Amazon offers a cutting-edge autonomous data labeling solution called Amazon SageMaker Ground Truth. This solution simplifies datasets for machine learning by providing a fully managed data labeling service.

You can easily create extremely precise training datasets with Ground Truth. You can label your data quickly and accurately using a specialized workflow. The program supports various labeling output formats, including text, pictures, video, and 3D cloud points.

Labeling capabilities make the labeling procedure simple and efficient, including automatic 3D cuboid snapping, 2D image distortion elimination, and auto-segment tools. They significantly shorten the labeling process for the dataset.

Heartex

Heartex offers a data labeling and annotations tool for building accurate and smart AI products. Heartex’s tool helps companies minimize the amount of time the team spends on preparing, analyzing, and labeling datasets for machine learning.

Sloth

Sloth is an open-source program for data labeling that was primarily created for computer vision research using the image and video data. It provides dynamic tools for computer vision data labeling.

This tool can be viewed as a framework or a collection of standard components that can be quickly combined to create a label tool that suits your requirements. Sloth allows you to label the data using custom configurations that you build yourself or predefined presets.

Sloth is relatively simple to employ. You can factorize and write your own visualization items. You can manage the entire procedure, including installation, labeling, and creating correctly referenced visualization datasets.

Playment

With the help of ML-assisted tools and advanced project management software, Playment’s multi-featured data labeling platform provides safe, individualized workflows for creating high-quality training datasets.

It provides annotations for various use scenarios, including sensor fusion annotation, picture annotation, and video annotation. With a labeling platform and an auto-scaling workforce, the platform provides end-to-end project management while maximizing the machine learning pipeline with high-quality datasets.

Incorporated quality control tools, automated labeling, centralized project management, workforce communication, dynamic business-based scaling, secure cloud storage, and other features are just a few of its characteristics. It’s a fantastic tool for labeling datasets and creating accurate, high-quality datasets for ML applications.

LightTag

LightTag is an additional text-labeling program made to produce specific datasets for NLP. The technology is set up to function in tandem with ML teams in a collaborative workflow. It provides a greatly simplified user interface (UI) experience to manage the workforce and facilitate annotations. Additionally, the program offers top-notch quality control tools for precise labeling and efficient dataset preparation.

Amazon Mechanical Turk

Amazon Mechanical Turk, also known as MTurk, is a well-known marketplace for crowdsourcing services frequently used for data tagging. You can create, publish, and manage various human intelligence activities (often referred to as HITs), such as text classification, transcriptions, or surveys, as a requester on Amazon Mechanical Turk. To describe your assignment, select consensus guidelines, and specify the amount you are ready to pay for each item, the MTurk platform offers helpful tools.

The MTurk platform has several disadvantages while being one of the market’s most affordable data labeling technologies. It lacks essential quality control features, to start. MTurk provides very little in the way of quality assurance, worker testing, or thorough reporting, in contrast to businesses like LionbridgeAI. MTurk requires requesters to manage their projects, including creating tasks and hiring workers.

Computer Vision Annotation Tool (CVAT)

Digital images and movies can be annotated using the Computer Vision Annotation Tool (CVAT). CVAT offers a wide range of functionality for labeling computer vision data, even though the program takes some time to learn and master. The program supports tasks like object detection, image segmentation, and image classification.

However, employing CVAT has a few disadvantages. One of the main drawbacks is the user interface, which can take a few days to get used to. Additionally, the utility only functions in Google Chrome. It hasn’t been tested in other browsers, which makes it challenging to carry out massive projects with numerous annotators. Additionally, development testing may be slowed since every quality check must be performed manually.

V7

The most powerful platform for computer vision training data is V7. V7 is a platform for automated annotation that combines dataset management, picture and video annotation, and training of an autoML model to carry out labeling tasks.

Automation of labeling, unmatched control over your annotation workflow, assistance in identifying data quality issues, and smooth pipeline integration are all features of V7. Additionally, it has a user experience that is on par with our obsessive attention to detail and superior technical assistance.

Labelbox

The correct annotation solution is provided by Lablebox for any activity, giving you complete visibility and control over every aspect of your labeling processes.

To expedite labeling without sacrificing quality, cutting-edge pre-labeling procedures are combined with solid automation technologies. In your labeling and review workflow, concentrate on human labeling, where it will have the most significant impact.

Their world-class labeling partners are fluent in more than 20 languages and have expertise in agriculture, fashion, medicine, and the life sciences. No matter your use case, they can assist you and have skilled teams ready on demand.

Doccano

A machine learning practitioner’s open-source annotation tool is called Doccano.

It offers job annotation features, including sequence labeling, sequence to sequence, and text classification. For sentiment analysis, named entity recognition, text summarizing, etc., Doccano allows you to create labeled data. A dataset can be made in a few hours. It has a collaborative annotation, support for several languages, smartphone compatibility, emoji compatibility, and a RESTful API.

Supervisely

Supervisely is a powerful platform for computer vision development, enabling lone researchers and big teams to experiment and annotate datasets and neural networks. It can be used with both a GPU and a CPU. Modern class-neutral neural networks for object tracking are built into the video labeling tool. It also has a REST API that allows for the integration of custom tracking NN. There are also OpenCV tracking, Linear, and Cubic interpolators.

Supervisely is the most excellent tool for labeling photos, videos, 3D point clouds, volumetric slices, and other data types. Using teams, workspaces, roles, and labeling jobs, you can manage and monitor annotation workflow at a large scale.

Using models from our Model Zoo or ones you create, train and use neural networks on your data. Integrating Python Notebooks and Scripts allow you to explore your data and automate routine operations.

Universal Data Tool

The Universal Data Tool offers tools and standards for creating, collaborating, labeling, and formatting datasets to enable anyone without a background in data science or engineering to make the next wave of potent, practical, and significant Artificial Intelligence applications. The Universal Data Tool is user-friendly, accessible, and developer-friendly.

With Universal Data Tool, you can:

• Integrate with already-existing applications
• Linux, Windows, and Mac can be downloaded and used as a desktop programs.
• Utilizes the open-source JSON data format for straightforward machine learning workflow integration
• It’s not necessary to upload data to the “cloud.”
• supports local files and online URLs
• simple for non-programmers to configure
• Fully open-source under the MIT license

Dataloop

The Dataloop platform enables the management of unstructured data (such as photos, audio files, and video files) and its annotation with various annotation tools (box, polygon, classification, etc.). Annotation work is completed in tasks, annotation tasks, or QA tasks, which enables the quality assurance process by allowing the original annotator to raise concerns and request corrections.

Dataloop automation lets you execute your own or open-source packages as services on various compute node types. With the help of the Dataloop pipelines, any business objective may be accomplished by combining services (add), people (in tasks), and models (for instance, pre-annotation).

Audino

A collaborative and cutting-edge open source tool for speech and audio annotation is called Audino. Annotators can use the tool to define and describe the temporal segmentation of audio files. A dynamically produced form makes it simple to label and transcript these portions. An admin can centrally manage user roles and project assignments through the dashboard. The dashboard also allows for label descriptions and value descriptions. For additional processing, the annotations can easily be exported in JSON format. Through a key-based API, the tool enables the upload and assignment of audio data to users. The annotation tool’s flexibility allows annotation for various tasks, including speech scoring, voice activity detection (VAD), speaker identification, speaker characterization, speech recognition, and emotion recognition. Thanks to the MIT open source license, it can be used for both professional and academic applications.

SuperAI

Super.AI is an AI-based data labeling platform that leverages both human expertise and AI technology to generate, organize, and label various forms of data. The platform utilizes a novel method of data labeling and machine learning called data programming, which is executed by their proprietary AI Compiler. The platform employs an assembly line-like approach to break down complex tasks into smaller, more manageable components, which are gradually automated over time.

Furthermore, the Super.AI compiler is capable of seamlessly converting computer code from one programming language to another without any manual intervention. This makes it ideal for data ingestion and analysis with machine learning, enabling developers to create large-scale machine learning applications rapidly and cost-effectively.

SurgeAI

Surge AI is a data labeling platform that uses lightning-fast labelers specifically designed for NLP’s complex challenges. Their platform integrates sophisticated quality controls, groundbreaking technology, and vibrant APIs to provide you with datasets that are infused with the richness and subtleties of language, and powerful tools to unify the labeling process.

Encord

Encord is a comprehensive AI-assisted platform for collaboratively annotating data, orchestrating active learning pipelines, fixing dataset errors, and diagnosing model errors & biases.

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Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications