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What is Supervisely?

Our leading-edge platform designed for the entire computer vision workflow enables a transformation from image annotation to accurate neural networks at speeds that can reach ten times faster than traditional methods. With our outstanding data labeling capabilities, you can turn your images, videos, and 3D point clouds into high-quality training datasets. This not only allows you to train your models effectively but also to monitor experiments, visualize outcomes, and continuously refine model predictions, all while developing tailored solutions in a cohesive environment. The self-hosted option we provide guarantees data security, offers extensive customization options, and ensures smooth integration with your current technology infrastructure. This all-encompassing solution for computer vision covers multi-format data annotation and management, extensive quality control, and neural network training within a single platform. Designed by data scientists for their colleagues, our advanced video labeling tool is inspired by professional video editing applications and is specifically crafted for machine learning uses and beyond. Additionally, with our platform, you can optimize your workflow and markedly enhance the productivity of your computer vision initiatives, ultimately leading to more innovative solutions in your projects.

What is NVIDIA DIGITS?

The NVIDIA Deep Learning GPU Training System (DIGITS) enhances the efficiency and accessibility of deep learning for engineers and data scientists alike. By utilizing DIGITS, users can rapidly develop highly accurate deep neural networks (DNNs) for various applications, such as image classification, segmentation, and object detection. This system simplifies critical deep learning tasks, encompassing data management, neural network architecture creation, multi-GPU training, and real-time performance tracking through sophisticated visual tools, while also providing a results browser to help in model selection for deployment. The interactive design of DIGITS enables data scientists to focus on the creative aspects of model development and training rather than getting mired in programming issues. Additionally, users have the capability to train models interactively using TensorFlow and visualize the model structure through TensorBoard. Importantly, DIGITS allows for the incorporation of custom plug-ins, which makes it possible to work with specialized data formats like DICOM, often used in the realm of medical imaging. This comprehensive and user-friendly approach not only boosts productivity but also empowers engineers to harness cutting-edge deep learning methodologies effectively, paving the way for innovative solutions in various fields.

Media

Media

Integrations Supported

Integrations Supported

Caffe
Dask
NetApp AIPod
TensorFlow
Torch
Unleash live

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Supervisely

Date Founded

2017

Company Location

United States

Company Website

supervise.ly/

Company Facts

Organization Name

NVIDIA DIGITS

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/digits

Categories and Features

Computer Vision

Blob Detection & Analysis
Building Tools
Image Processing
Multiple Image Type Support
Reporting / Analytics Integration
Smart Camera Integration

Data Annotation

Not specified

Data Labeling

Labeling Automation
Labeling Quality
Supports Audio Files
Task Management
Team Collaboration

Image Annotation

Not specified

Image Recognition

Not specified

Machine Learning

Not specified

Neural Network

Not specified

Video Annotation

Not specified

Categories and Features

Deep Learning

Not specified

Neural Network

Not specified

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