The Universal Data Tool is a User Interface for editing and annotating Images (Computer Vision, Bounding Boxes, Segmentation), Text (Named Entity Recognition, Classification) or general purpose data entry. to view and edit any data defined in the extensible the .udt.
- - UniversalDataTool is the most popular Web-based, Windows, Mac & Linux alternative to Segments.ai.
- - UniversalDataTool is the most popular Open Source & free alternative to Segments.ai.
- Is this is a good alternative?YesNo18 Computer Vision Annotation Tool (CVAT) alternatives
- Freemium • Open Source
CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different...
A desktop application with a UX built for through-put, HyperLabel is a complete toolset for quality labeling process management and training data creation.
doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks.
- - doccano is the most popular Self-Hosted alternative to Segments.ai.
A family of foundation models producing universal features suitable for image-level visual tasks (image classification, instance retrieval, video understanding) as well as pixel-level visual tasks (depth estimation, semantic segmentation).
A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
- - Label Box is the most popular commercial alternative to Segments.ai.
Labeling AI is a deep learning-based auto labeling solution that develops and auto-labels custom AI by learning minimal manual labeling data. Labeling AI is an innovative tool that can save your time.
- - Labeling AI is the most popular SaaS alternative to Segments.ai.
Supervisely helps people with and without machine learning expertise to create state-of-the-art computer vision applications. We care about entire workflow from raw data to building and deploying neural networks for your special task without coding.
Amazon SageMaker enables you to label raw data, such as images, text files, and videos, and generate labeled synthetic data to create high-quality datasets for training machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon...
Amazon SageMaker Data Labeling Features
Data labeling tool for various data types with a customizable interface, annotation team management, and automatic labeling through active learning.