10 Data Labeling Tools to Check in 2025

Data labeling tools are software designed to label raw data in various formats like text, images, and audio formats to train machine learning models. These tools often come with a user-friendly interface where human labelers can view the raw data and add labels.

According to a McKinsey report, data labeling is one of the most challenging aspects of training an ML model. [6] Data labeling tools can help simplify this process and generate high-quality data necessary to effectively train ML models.

Most large organizations with the necessary resources create their data labeling tools in-house. However, due to this approach’s time-wastage and cost implications, most small businesses opt for off-the-shelf software solutions.

Some of the software solutions are available as free packages, but the most advanced ones come as paid packages. The main difference between the two choices is their effectiveness and applicability. Most free software solutions only offer basic labeling instruments, which may not be sufficient to label complex data sets. Premium software solutions, on the other hand, offer additional customization options and APIs.

Data labeling vs data annotation

While often used interchangeably, data labeling and data annotation serve distinct purposes in machine learning. Think of labeling as putting simple tags on data, while annotation is more like adding detailed notes and context. Let’s explore how these processes differ and when to use each one.

What is Data Labeling?

At its core, data labeling is straightforward: you’re assigning predefined categories to your data. Imagine sorting emails into “spam” or “not spam,” or categorizing images as “cat” or “dog.” It’s quick, simple, and focused on basic classification tasks.

What is Data Annotation?

Data annotation takes things a step further. Rather than just applying labels, you’re enriching the data with detailed information. For instance, when working with images, you might draw boxes around objects, outline specific regions, or describe relationships between different elements. It’s like adding comprehensive footnotes to your data.

How Do They Differ?

In terms of scope, labeling keeps things simple with basic categorization, while annotation provides a richer layer of context and meaning. This difference in depth affects how they’re used: labeling works well for straightforward tasks like sentiment analysis or basic image classification, while annotation is crucial for complex applications like autonomous vehicles or detailed image analysis.

The complexity and time investment also vary significantly. Labeling is generally quick and straightforward – perfect for projects needing rapid categorization. Annotation, however, requires more time and expertise to capture nuanced details and relationships within the data.

When to use each approach?

Choose labeling when you need:

• Quick classification of data into clear categories
• Basic sentiment analysis
• Simple image or text categorization

Choose annotation when your project requires:

• Detailed object detection in images
• Complex relationship mapping between data elements
• Comprehensive context for advanced machine learning models

The choice between labeling and annotation isn’t always black and white – some projects might benefit from a combination of both approaches, depending on their specific requirements and goals.

Read more: Data Annotation Services

10 Data Labeling Tools to consider

1. Label Studio

Label studio is a web application that offers data labeling and exploration services for different types of data, including text, image, and audio files. It has a python-base backend and a React and MST front end. This unique feature supports all browsers and can be incorporated into different applications.

Source: labelstud.io

The streamlined UI, along with its multi-data support capabilities, makes it suitable for all ML applications, and the end results (labeled data sets) are pretty accurate, too.

2. Sloth

Sloth is an open-source data labeling program created specifically for handling computer vision data annotation applications. You can use the tool as a framework or a collection of standard components that can be combined effortlessly to meet your data labeling requirements.
Sloth is relatively easy to use. It gives you control over all features and capabilities, including custom features and predefined presets, making the data labeling process much easier.

3. Tagtog

Tagtog is a text-based data annotation tool specifically designed for handling text formats. It has a pretty user-friendly user interface that allows you to label data and manage the labeling process through integrated features that further enhance the processing speed.

Source: tagtog.com

4. Audino

Audino is an open-source audio annotation program. The program comes with a key-based API that enables you to upload and assign data to multiple users. This feature makes it perfect for handling huge data annotation tasks that require multiple human labelers.

This audio annotation program also offers extensive flexibility. It enables you to perform various tasks such as speaker identification, speech recognition, characterization, and voice activity detection. Unfortunately, its numerous features and complex UI may make it difficult for beginners to use effectively.

5. SuperAnnotate

SuperAnnotate is a comprehensive data labeling and annotation platform designed for computer vision tasks. It supports image, video, and LiDAR data labeling, offering tools for bounding boxes, polygons, segmentation, and more. SuperAnnotate also includes project management features, quality assurance workflows, and collaboration tools, making it ideal for teams handling large datasets.

6. Diffgram

Diffgram is an open-source data labeling tool with robust automation and collaboration features. It supports a wide range of data formats, including images, video, text, and 3D datasets. With its ability to integrate with machine learning workflows, Diffgram is suitable for advanced AI projects. It also provides APIs and custom integrations, allowing seamless deployment in various environments.

7. V7

V7 is a data labeling platform optimized for image and video annotation tasks. It includes advanced tools such as instance segmentation, keypoint annotation, and text recognition. V7 also incorporates AI-assisted labeling to speed up the annotation process and ensure higher accuracy. Its ability to integrate with Python SDKs and ML pipelines makes it ideal for enterprise use cases.

8. Dataloop

Dataloop is a data labeling and data management platform that supports image, video, and audio annotation. It features AI-powered labeling tools, automated workflows, and analytics dashboards to streamline the labeling process. Dataloop also offers a cloud-based platform, enabling easy collaboration and scalability for large projects.

9. CVAT (Computer Vision Annotation Tool)

CVAT is an open-source annotation tool developed by Intel for labeling image and video datasets. It supports tasks such as object detection, instance segmentation, and lane marking. CVAT is highly customizable and provides advanced features like interpolation for video annotation and shortcuts for faster labeling, making it a popular choice among researchers and developers.

10. Prodi.gy

Prodi.gy is a lightweight data labeling tool tailored for text and image annotation tasks. Created by the team behind the popular NLP library spaCy, Prodi.gy is known for its scripting capabilities and ease of integration with Python workflows. It is ideal for creating custom training datasets for NLP and computer vision models.

Read more: Top 8 Open-Source Big Data Tools for 2025