Supervisely is described as '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' and is an website. There are more than 25 alternatives to Supervisely, not only websites but also apps for a variety of platforms, including SaaS, Windows, Self-Hosted and Linux apps. The best Supervisely alternative is Oslovision, which is free. Other great sites and apps similar to Supervisely are UniversalDataTool, Argilla, Amazon SageMaker Data Labeling and VGG Image Annotator (VIA).
Image annotation tool for modern computer vision. Easy to use, great for teams of all sizes. Hooks into training pipelines, designed around the latest State of the Art computer vision models.
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.
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 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).
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.
Cloud AutoML helps you easily train high quality custom machine learning models with limited machine learning expertise needed.
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