Top 10 Best Labelling Software of 2026

Labelbox is an enterprise-grade data labeling platform that enables teams to annotate images, videos, text, sensor fusion, and other data types for machine learning projects. It offers customizable ontologies, automation tools like model-assisted labeling, and robust quality control features to ensure high annotation accuracy at scale. The platform supports collaborative workflows, active learning integration, and versioning for efficient data pipelines.

Scale AI is a comprehensive data labeling platform designed to generate high-quality annotated datasets for training machine learning models across computer vision, NLP, audio, and 3D sensor data. It offers advanced annotation tools like bounding boxes, polygons, semantic segmentation, and text classification, powered by a global workforce of experts and AI-assisted workflows. The platform excels in scalability, quality assurance through consensus labeling and active learning, and seamless integrations with popular ML frameworks.

Label Studio is an open-source data labeling platform that enables teams to annotate various data types including images, text, audio, video, and time-series data for machine learning projects. It offers a flexible, web-based interface for creating custom annotation workflows using a simple XML-like configuration language. The tool supports collaborative labeling, active learning integrations, and exports to numerous ML-compatible formats.

V7 is an advanced AI-powered data labeling platform specializing in computer vision tasks, allowing users to annotate images, videos, documents, and 3D data with precision. It features automated annotation tools driven by state-of-the-art models like Segment Anything (SAM) to accelerate workflows and reduce manual effort. The platform supports collaborative team environments, custom workflows, and seamless integrations with ML frameworks for end-to-end model training.

Supervisely is an end-to-end platform for computer vision annotation, supporting images, videos, 3D point clouds, and medical data with advanced tools like polygons, brushes, cuboids, and AI-assisted labeling. It enables collaborative workflows, dataset management, and integration with ML training pipelines. The platform offers both cloud and on-premise deployment options for scalability.

Prodigy by Explosion AI is a scriptable annotation tool optimized for labeling text, images, and categorical data in NLP and ML workflows. It excels in active learning, where suggestion models prioritize uncertain examples to streamline annotation and reduce manual effort. Fully customizable via Python recipes, it integrates natively with spaCy for efficient, local data labeling without cloud dependencies.

CVAT (Computer Vision Annotation Tool) is an open-source, web-based platform for annotating images and videos tailored for computer vision machine learning projects. It supports a wide array of annotation types including bounding boxes, polygons, polylines, keypoints, and cuboids, with advanced features like object tracking in videos and interpolation. The tool enables collaborative workflows, integrates with ML models for semi-automatic labeling, and exports data in standard formats like COCO, YOLO, and Pascal VOC.

SuperAnnotate is an end-to-end platform for annotating training data for AI and machine learning models, supporting images, videos, documents, and text with tools like bounding boxes, polygons, semantic segmentation, keypoints, and NER. It streamlines workflows through team collaboration, automated quality assurance, and model-assisted labeling to ensure high accuracy and efficiency. Ideal for computer vision and NLP projects, it integrates with popular ML frameworks and offers scalable project management.

Encord is a data-centric AI platform specializing in computer vision annotation and management for images and videos. It provides advanced tools for object detection, semantic segmentation, keypoint annotation, and video tracking, alongside data curation, active learning, and quality control features. Designed for enterprise-scale ML workflows, it emphasizes collaboration, automation, and dataset optimization to improve model performance.

Doccano is an open-source, web-based annotation tool primarily designed for labeling text data in NLP tasks such as text classification, named entity recognition (NER), sequence-to-sequence, and relation extraction. It enables collaborative annotation through multi-user projects, with support for importing data from various sources and exporting in formats like JSON, CSV, and CoNLL. The tool is self-hosted via Docker or direct installation, making it suitable for teams handling custom datasets without vendor lock-in.