What is Data Labeling Software?

Data labeling software labels or annotates data for training machine learning models. Machine learning algorithms rely on large amounts of labeled data to learn patterns and make predictions. Data labeling solutions help humans identify and label the relevant features and characteristics of the data that will be used to train the machine learning model. Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data, and many data labeling platforms now extend that same tooling to computer vision tasks like bounding boxes and point cloud annotation for 3D and LiDAR data. Data labeling software is used in various applications, including natural language processing, image and video classification, and object detection. It is an important tool in the development and training of machine learning models and plays a critical role in their accuracy and effectiveness.

What types of data labeling software exist?

Selecting a data labeling software requires a prior evaluation and understanding of data-driven workflows in your business. Below are the types of software you can consider. Manual labeling software: These data labeling platforms segment, label, and classify data with the help of a "human in the loop" service. Human annotators label the training data based on businesses' geographic locations. The data annotation service is extended to the ML model development workflow, and labeling data becomes more effective. Automated labeling software: The automated data labeling software preprocesses raw datasets consisting of text, images, liDAR data, DICOM, PDF, or audio using an unsupervised learning approach. The algorithm assigns labels and categories to data without referring to external annotators. Active learning labeling software: Also known as active learning tools, these are semi-supervised tools that follow a "query-based" approach to labeling data. Based on the uncertainty score, they query data using manual or annotator labeling. For more challenging labels, they prompt the human annotator with queries. Crowdsource labeling software: These data labeling platforms crowd data labeling services to a crowd of developers to train high-quality data pipelines. Custom data labeling can be ideal for large or enterprise-sized teams. Integrated labeling and model training software: These tools provide combined services for data labeling and predictive modeling. Using advanced data analysis, users can label, train, and build machine learning models to optimize their production cycles.

What are the Common Features of Data Labeling Software?

Based on G2 reviews, data scientists and machine learning engineers evaluate data labeling software by comparing annotation tool variety, model-assisted labeling speed, and collaboration features. There are several features that are often included in data labeling software, including: Label assignment: Data labeling software allows users to assign labels or tags to specific data points, such as text, images, or videos. Annotation tools: Some data labeling software includes tools for annotating data, such as bounding boxes, polygon drawing tools, cloud points, keymakers, and point annotation tools. These tools can be used to highlight specific features or characteristics of the data. Machine learning algorithms: Some data labeling software uses machine learning algorithms to automate the labeling process or generate initial labels for data, which humans can then review and correct as needed. Data management and organization: Data labeling software often includes features for organizing and managing large datasets, such as the ability to filter and search for specific data points, track progress and completion, and generate reports. Collaboration tools: Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users, track changes and revisions, and review and discuss data labeling decisions. Integration with data science and machine learning platforms: Some data labeling software is designed to integrate with popular data science and machine learning platforms, such as TensorFlow or PyTorch, making it easier to use the labeled data to train machine learning models. Image, text, audio, or video annotation: These tools comply with multiple unstructured data formats to train and validate models designed to generate output in images, text, video, audio, PDF, and so on.

Page 2 | Best Data Labeling Software: User Reviews from January 2026

FiftyOne

FiftyOne by Voxel51 is the leading data platform for physical AI. Without the right data, even the smartest AI models fail. FiftyOne gives machine learning engineers the power to deeply understand and evaluate their visual datasets—across images, videos, 3D point clouds, geospatial, and medical data. With over 2.8 million open source installs and customers like Walmart, GM, Bosch, Medtronic, and the University of Michigan Health, FiftyOne is an indispensable tool for building computer vision systems that work in the real world, not just in the lab. FiftyOne, combines open-source flexibility with enterprise-grade capabilities to help teams understand and analyze their multimodal data, annotate the right samples, close quality and coverage gaps, and build models that perform reliably in the real world. Proven impact with FiftyOne: ⬆️30% increase in model accuracy ⏱️5+ months of development time saved 📈30% boost in team productivity Learn more about FiftyOne: ✏️Annotation: Adopt smart data selection techniques with auto-labeling and manual workflows to curate first and prioritize the most valuable data to label. 🔍Data Curation and Management: Explore and curate your datasets with precision. Get insights into distribution, diversity, coverage, and more to optimize AI performance. Analyze billions of samples, hosted securely on your infrastructure, whether in the cloud or on-premise. 📊Model Evaluation: Quickly identify what’s driving model failures or successes. From aggregate performance metrics to sample-level diagnostics, diagnose failure modes and edge cases preventing your models from reaching optimal performance in production. At Voxel51, we empower hundreds of thousands of ML engineers around the world to unlock data insights to maximize model performance.

Average Rating: 4.5/5.0

Total Reviews: 24

How Do G2 Users Rate FiftyOne?

Ease of Use: 8.3/10 (Category avg: 8.8/10)

Who Is the Company Behind FiftyOne?

Seller: Voxel51
Year Founded: 2018
HQ Location: Ann Arbor, US
Twitter: @Voxel51
1,624 Twitter followers
LinkedIn® Page: www.linkedin.com
63 employees on LinkedIn®

Who Uses This Product?

Top Industries: Computer Software
Company Size: 71% Small, 25% Medium

What Are Recent G2 Reviews of FiftyOne?

"Streamlined Workflows with Open-Source Flexibility"

Rating: 4.5/5.0 stars

— Josh h.

Best Data Labeling Software - Page 2

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