Heartex is described as 'Data labeling tool for various data types with a customizable interface, annotation team management, and automatic labeling through active learning' and is an app. There are more than 10 alternatives to Heartex for a variety of platforms, including SaaS, Web-based, Windows, Self-Hosted and Mac apps. The best Heartex alternative is UniversalDataTool, which is both free and Open Source. Other great apps like Heartex are Amazon SageMaker Data Labeling, VGG Image Annotator (VIA), HyperLabel and doccano.
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.
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...
VGG Image Annotator is a simple and standalone manual annotation software for image, audio and video. VIA runs in a web browser and does not require any installation or setup.
A desktop application with a UX built for through-put, HyperLabel is a complete toolset for quality labeling process management and training data creation.
A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
A downloadable annotation tool for NLP and computer vision tasks such as named entity recognition, text classification, object detection, image segmentation, A/B evaluation and more.
Deep Block is the world's fastest AI-powered remote sensing imagery analysis solution. Train your own AI models to detect instantly any objects in large satellite, aerial, and drone images. With its no-code data labeling interface, you can achieve your MLOps projects in...
The platform for computer vision experts to iterate more quickly between data labeling, model training and failure case discovery.
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