MatConvNet

MatConvNet

VLFeat
+
+

Related Products

  • RunPod
    220 Ratings
    Visit Website
  • Google Compute Engine
    1,166 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    983 Ratings
    Visit Website
  • Servers.com by Nexcess
    15 Ratings
    Visit Website
  • Windocks
    7 Ratings
    Visit Website
  • Gr4vy
    6 Ratings
    Visit Website
  • KrakenD
    71 Ratings
    Visit Website
  • Google Cloud SQL
    553 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website

About

Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver the highest performance in Amazon EC2 for deep learning and high-performance computing applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce the cost to train ML models by up to 40%. These instances help you iterate on your solutions at a faster pace and get to market more quickly. You can use P5, P5e, and P5en instances for training and deploying increasingly complex large language models and diffusion models powering the most demanding generative artificial intelligence applications. These applications include question-answering, code generation, video and image generation, and speech recognition. You can also use these instances to deploy demanding HPC applications at scale for pharmaceutical discovery.

About

The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local features extraction and matching. Algorithms include Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs. Many pre-trained CNNs for image classification, segmentation, face recognition, and text detection are available.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Organizations looking for a solution to accelerate their deep learning and high-performance computing applications

Audience

Anyone in need of a deep learning software

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Amazon
Founded: 1994
United States
aws.amazon.com/ec2/instance-types/p5/

Company Information

VLFeat
United States
www.vlfeat.org/matconvnet/

Alternatives

Alternatives

DataMelt

DataMelt

jWork.ORG
LiveLink for MATLAB

LiveLink for MATLAB

Comsol Group
MATLAB

MATLAB

The MathWorks

Categories

Categories

Deep Learning Features

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Integrations

AWS Deep Learning Containers
AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 Capacity Blocks for ML
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Amazon FSx
Amazon S3
Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
TensorFlow

Integrations

AWS Deep Learning Containers
AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 Capacity Blocks for ML
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Amazon FSx
Amazon S3
Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
TensorFlow
Claim Amazon EC2 P5 Instances and update features and information
Claim Amazon EC2 P5 Instances and update features and information
Claim MatConvNet and update features and information
Claim MatConvNet and update features and information