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VisoLabel

Name: VisoLabel
Author: VisoLabel

Unpublished

VisoLabel is a extensive local-first computer vision platform that enables users to annotate images and videos using advanced assisted labeling and auto-annotation techniques, then train object detection, segmentation, and recognition models directly on their hardware or via cloud integrations such as Google Colab. Its main features include one-click installation across Windows, macOS, and Linux without need for Docker, seamless SAM2 and SAM3 assisted labeling tools for efficient data annotation, and local training capabilities that ensure data privacy, speed, and reproducibility, making it ideal for machine learning practitioners, data scientists, AI researchers, and development teams seeking a secure, fast, and easy-to-use solution for preparing training datasets and deploying computer vision models without relying on cloud services or complex setup processes.

VisoLabel can be found in Data Labeling & Annotation categories.

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Details

Pricing: $0-$49/mo

Consumer Type: B2B

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VisoLabel Main Features

Local training
Train models like YOLO and segmentation on your own GPU without needing internet or sending data outside.
Fast and private
Everything happens on your computer quickly, privately, and in the same place.
One-click installer
You can install VisoLabel easily with one click on Windows, macOS, or Linux, without needing Docker.
SAM3 auto-annotation
Let SAM3 automatically label thousands of images so you only review the tricky parts.
Google Colab pipeline
If you need more power, you can send training jobs to Google Colab with just one click.
Image & video annotation
You can label images and videos frame-by-frame with object tracking, using the same shortcuts for convenience.
SAM2 assisted annotation
Use SAM2 to automatically draw shapes on images with just one click, helping you label faster.
Local-first annotation & training
You can annotate and train models on your own computer without using the internet or sending data to others.

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