Top 10 Data Labeling Tools for 2023

The data labeling aspects are crucial in machine learning and AI development. A structured set of data training in an ML system is necessary. It takes a lot to create accurately labeled datasets. Data labeling tools are very useful because they can automate labeling, which is extremely tedious.

Data labeling tools allow for easier collaboration and quality control during the entire dataset creation process. It is possible to create a training dataset using any data type and connect it with your ML pipelines. In this article, we will explore the top 10 data labeling tools.

Top 10 Data Labeling Tools for 2023

1. Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth, a state-of-the-art data labeling service by Amazon, is available. This tool simplifies the creation of machine learning datasets by providing a fully managed data-labeling service.

Ground Truth makes it easy to create highly accurate training data sets. Ground Truth has a built-in workflow that allows you to label your data in minutes with high accuracy. This tool supports different types of labeling output, including text, images videos, and 3D cloud points.

Labeling features like automatic 3D cuboid snapping, removals of distortion in 2D images, and auto-segment tools make labeling easy and efficient. These features greatly reduce the time required to label the dataset.

Key of features:

• Amazon allows you to enter raw data.
• Use the built-in workflow to create automatic labeling tasks.
• Select the right labeler from the group.
• Assistive labeling feature for labels
• Create accurate training datasets.

These are the benefits:

• It is easy to use and automatic.
• It increases data labeling accuracy.
• This feature allows for a significant reduction in time.

Also read: Data Discovery: What It Is, Uses and Tools

2. Label Studio

Label Studio is a web platform that allows you to explore multiple data types and offer data labeling services. It is built with a mixture of MST and React as the frontend and Python as its backend.

It allows data labeling of all data types: text, images, and video as well as audio and time series. The resulting datasets are highly accurate and can be easily used in ML applications. It is available from all browsers. It is available as precompiled CSS/JS scripts that can be used on all browsers. You can embed Label Studio UI in your applications.

This tool is used to accurately label and create optimized datasets.

Key of features:

• Data is taken from different APIs, files, and HTML markup.
• Pipelines data to a labeling structure that includes three main sub-processes
• Data entry task that collects data from different sources.
• The final step results in labeling in JSON format.
• Optional labeling results can be obtained in JSON format by the prediction process.
• Machine learning backend uses efficient and popular ML frameworks to automatically create precise datasets.

These are the benefits:

• Data labeling for different types of data is possible.
• It is easy to use and it works automatically
• It is accessible from any web browser and also can be embedded in personal applications.
• High-level dataset with precise labeling workflow.

3. Sloth

Sloth is an open-source data labeling tool that was primarily designed for the labeling of image and video data in computer vision research. It provides dynamic tools for data labeling in the field of computer vision.

This tool can be used as a framework or as a collection of standard components that allow you to quickly create a label tool tailored to your requirements. Sloth allows you to create custom configurations or use pre-made configurations to label data.

You can create and factorize your visualization items. The entire process, from installation to labeling and creation of properly documented visualization datasets can be handled by you. Sloth is very easy to use.

These are the benefits:

• It makes it easy to label video and image data.
• A specialized tool for creating accurate data sets for computer vision.
• To create your labeling workflow, you can modify the default configurations.

4. Labelbox

LabelBox is a well-known data labeling tool. It offers an iterative workflow process that allows for precise data labeling and creates optimized datasets. This platform interface allows machine learning teams to communicate easily and create datasets in a collaborative environment. This tool provides a command center to manage and execute data management tasks, data labeling, and data analysis tasks.

Key of features:

• Management of the external labeling service, workers, and machine labels.
• Optimization for different data types.
• An analytical and automatic iterative process to train and label data, make predictions and perform active learning.

These are the benefits:

• For ML teams to collaborate, centralized command center.
• It is easy to complete tasks and communicate well.
• Active learning can be used to improve your labeling accuracy and generate better datasets.

5. Tagtog

Tagtog is a data-labeling tool that allows text-based labeling. To create text-based AI-specific datasets, the labeling process is optimized to work with text formats and text-based operations.

The tool’s core is a Natural Language Processing tool for text annotation. It can also be used to manually label text or to use machine learning models to optimize it.

This tool automatically extracts relevant information from text. It can help you identify patterns and challenges and find solutions. It supports ML and dictionary annotations in multiple languages and formats. Secure Cloud storage is available, as well as team collaboration and quality control.

Key of features:

• You can import text-based data into any file format.
• You can either label automatically or manually.
• With API format, export accurate data.

These are the benefits:

• It is easy to use and accessible to all.
• Flexible, it can be integrated into your own application with a customized workflow and workforce.
• It is both times- and expense-efficient.

6. Playment

Playment is a multi-featured data-labeling platform, that offers customizable and secure workflows for creating high-quality training datasets using ML-assisted tools. It also includes sophisticated project management software.

There are many annotations available for different use cases such as image annotation, sensor fusion annotation, and video annotation. It supports project management from start to finish with a labeling platform and an auto-scaling workforce. This allows you to optimize your machine-learning pipeline using high-quality datasets.

It features workflow customization, automated labeling, and centralized project management. There are also built-in quality control tools. Dynamic business-based scaling is possible. Secure cloud storage is available. It is a great tool for labeling your data and producing high-quality, accurate datasets for ML apps.

These are the benefits:

• All-in-one project management tool.
• Collaboration platform to allow ML teams to seamlessly collaborate.
• It is easy to use and automated with built-in tools.
• Quality control is a key focus.

7. Dataturk

Dataturk is an open-source online tool, that provides services for labeling text, images, and video data. The tool makes it easy to upload data, collaborate with others, and then start tagging. It allows you to quickly create accurate datasets in a matter of hours.

It supports many data annotation requirements, including Image Bounding Boxes and NER tagging in files, Image Segmentation, POS tagging, and more. Simple UI to facilitate workforce collaborations.

Key of features:

• You can create a project using the required annotation.
• Upload the data in any format.
• Start tagging/labeling the workforce.

These are the benefits:

• Open-source software means that all can access the services.
• A simple UI platform to coordinate team and labeling.
• Highly simplified labeling process to create datasets in a short period of time.

Also read: What is Data Vault Modeling and How Can You Use It?

8. LightTag

LightTag is another text-labeling tool, that is designed to produce accurate datasets for NLP. It can be used in a collaborative workflow alongside ML teams. It provides a simplified UI to help manage your workforce and simplify annotations. It also provides high-quality control features that allow for precise labeling and optimized data creation.

These are the benefits:

• A super simplified UI platform allows for team management and labeling.
• Data labeling faster and more efficiently without complex features
• This reduces the time required for project management and costs.

9. Superannotate

Superannotate, the fastest data annotation tool, is specifically designed to provide a complete solution for computer-vision products. It provides an integrated platform that allows you to label, train and automate your computer vision pipeline. It allows for multi-level quality control and collaboration to improve model performance.

It integrates easily with any platform for a seamless workflow. It can label images, videos, LiDar text/NLP, as well as audio data. This tool is equipped with powerful tools, automated predictions, and quality control to speed up the annotation process.

These are the benefits:

• Smart predictions and active learning are supported by the platform to generate more precise datasets.
• Transfer learning is used to increase the effectiveness of overall training.
• It can be used for manual and automatic labeling, with a quality assurance structure.

10. CVAT

CVAT is an open-source tool that allows you to label objects in computer vision. It supports video and image annotations. CVAT is useful for image segmentation, object classification, and other tasks. Although the tool is quite powerful, it can be difficult to use.

It is not easy to grasp the overall workflow and specific use cases. This tool requires training. CVAT can only be accessed via the Google Chrome browser. It is not easy to learn the web interface. Although the tool is effective in labeling and data generation, it lacks quality control mechanisms. You will need to manually do this.

These are the benefits:

• This tool is free and open-source and can be used to create annotations based on images and videos.
• It allows for automatic labeling.

Conclusion

Machine learning and AI have always relied on data annotation or labeling. Prior to the advent of data labeling tools, manually labeling data points in a dataset was difficult, inefficient, and error-prone.

Automation, team management, and prediction analysis make the process much simpler. Datasets can be optimized and made more precise by incorporating different variables.

These tools make it easier to work as data scientists or ML developers. There are many datasets available for various applications. The ability to pipeline labeled datasets into machine learning models has made the creation of AI-based work and model optimization easier, faster, and more precise.