MatConvNet

MatConvNet

VLFeat
+
+

Related Products

  • Qloo
    23 Ratings
    Visit Website
  • Nutrient SDK
    108 Ratings
    Visit Website
  • Apify
    1,242 Ratings
    Visit Website
  • DXcharts
    28 Ratings
    Visit Website
  • Vertex AI
    961 Ratings
    Visit Website
  • pCloud Business
    182 Ratings
    Visit Website
  • SDS Manager
    4 Ratings
    Visit Website
  • Highcharts
    123 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • Emtrain
    41 Ratings
    Visit Website

About

ConvNetJS is a Javascript library for training deep learning models (neural networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows you to formulate and solve neural networks in Javascript, and was originally written by @karpathy. However, the library has since been extended by contributions from the community and more are warmly welcome. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alternatively, you can also choose to download the latest release of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder.

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

Developers, professionals and researchers seeking a solution for training deep learning models

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

ConvNetJS
cs.stanford.edu/people/karpathy/convnetjs/

Company Information

VLFeat
United States
www.vlfeat.org/matconvnet/

Alternatives

Alternatives

LiveLink for MATLAB

LiveLink for MATLAB

Comsol Group
DataMelt

DataMelt

jWork.ORG
MATLAB

MATLAB

The MathWorks
Deci

Deci

Deci AI

Categories

Categories

Deep Learning Features

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

Integrations

Qwen3-Omni

Integrations

Qwen3-Omni
Claim ConvNetJS and update features and information
Claim ConvNetJS and update features and information
Claim MatConvNet and update features and information
Claim MatConvNet and update features and information