HyperLabel is described as 'A desktop application with a UX built for through-put, HyperLabel is a complete toolset for quality labeling process management and training data creation' and is an app. There are more than 25 alternatives to HyperLabel for a variety of platforms, including Web-based, SaaS, Self-Hosted, Linux and Windows apps. The best HyperLabel alternative is UniversalDataTool, which is both free and Open Source. Other great apps like HyperLabel are Argilla, Amazon SageMaker Data Labeling, VGG Image Annotator (VIA) and Meta SAM 2.
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.
SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video.
Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms.
doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks.
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).
quicklabel is a native, streamlined GUI tool for manual classification of prompt/image pairs, meant for hobbyists. Outputs dreambooth-style finetuning datasets, as taken by the kohya_ss finetuning scripts.
Training Mule allows you or your team to easily label images, providing you with the datasets that you require for the best results. With your images labelled, we will train the network of your choice and provide you with the model ready for use.
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.
A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.
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