MATLAB vs. MatConvNet vs. scikit-learn Comparison


MATLAB The MathWorks	MatConvNet VLFeat	scikit-learn	+
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About MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Automatically convert MATLAB algorithms to C/C++, HDL, and CUDA code to run on your embedded processor or FPGA/ASIC. MATLAB works with Simulink to support Model-Based Design.	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.	About Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.
Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook	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 Modeling, simulation, and programming software that can be hosted in the cloud or on-premise	Audience Anyone in need of a deep learning software	Audience Engineers and data scientists requiring a solution to manage and improve their machine learning research
Support Phone Support 24/7 Live Support Online	Support Phone Support 24/7 Live Support Online	Support Phone Support 24/7 Live Support Online
API Offers API	API Offers API	API Offers API
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Pricing No information available. Free Version Free Trial	Pricing No information available. Free Version Free Trial	Pricing Free Free Version Free Trial
Reviews/Ratings Overall 4.7 / 5 ease 4.5 / 5 features 4.9 / 5 design 4.8 / 5 support 4.8 / 5 Read all reviews	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	Training Documentation Webinars Live Online In Person
Company Information The MathWorks Founded: 1984 United States www.mathworks.com	Company Information VLFeat United States www.vlfeat.org/matconvnet/	Company Information scikit-learn United States scikit-learn.org/stable/
Alternatives Posit	Alternatives IBM Watson Machine Learning Accelerator IBM	Alternatives Gensim Radim Řehůřek
PrimeSim HSPICE Synopsys	LiveLink for MATLAB Comsol Group	ML.NET Microsoft
LabVIEW NI	DataMelt jWork.ORG	MLlib Apache Software Foundation
LiveLink for MATLAB Comsol Group	ConvNetJS	Dask
RunMat Dystr View All	MATLAB The MathWorks View All	Keepsake Replicate View All
Categories Backtesting CAD Computer-Aided Engineering (CAE) Data Science Data Visualization Embedded Software Development Engineering Financial Risk Management Fraud Detection Mechanical Engineering Predictive Analytics Programming Languages Simulation Statistical Analysis	Categories Deep Learning	Categories Machine Learning
Show More Features CAD Features 2 1/2-Axis Milling 2D Drawing 3-Axis Milling 3D Modeling 4-Axis Milling 5-Axis Milling Civil Collaboration Database Connectivity Design Analysis Design Export Document Management Electrical Hole Making Mechanical Mechatronics Presentation Tools Simulate Cycles Spiral Output Structural Engineering Toolpath Simulation User Defined Cycles Engineering Features 2D Drawing 3D Modeling Chemical Engineering Civil Engineering Collaboration Design Analysis Design Export Document Management Electrical Engineering Mechanical Engineering Mechatronics Presentation Tools Structural Engineering Financial Risk Management Features Compliance Management Credit Risk Management For Hedge Funds Liquidity Analysis Loan Portfolio Management Market Risk Management Operational Risk Management Portfolio Management Portfolio Modeling Risk Analytics Benchmarks Stress Tests Value At Risk Calculation Simulation Features 1D Simulation 3D Modeling 3D Simulation Agent-Based Modeling Continuous Modeling Design Analysis Direct Manipulation Discrete Event Modeling Dynamic Modeling Graphical Modeling Industry Specific Database Monte Carlo Simulation Motion Modeling Presentation Tools Stochastic Modeling Turbulence Modeling Statistical Analysis Features Analytics Association Discovery Compliance Tracking File Management File Storage Forecasting Multivariate Analysis Regression Analysis Statistical Process Control Statistical Simulation Survival Analysis Time Series Visualization	Show More Features Deep Learning Features Convolutional Neural Networks Document Classification Image Segmentation ML Algorithm Library Model Training Neural Network Modeling Self-Learning Visualization
Integrations COMSOL Multiphysics CUDA Codebeamer Codecov CoppeliaSim Devin Domino Enterprise MLOps Platform Flower Fuzzball Intel Tiber AI Studio JMP Statistical Software Mayhem Code Security NVIDIA TensorRT OpenCV OptSim Overleaf RunMat TPT Tronis Visplore Show More Integrations View All 77 Integrations	Integrations COMSOL Multiphysics CUDA Codebeamer Codecov CoppeliaSim Devin Domino Enterprise MLOps Platform Flower Fuzzball Intel Tiber AI Studio JMP Statistical Software Mayhem Code Security NVIDIA TensorRT OpenCV OptSim Overleaf RunMat TPT Tronis Visplore Show More Integrations	Integrations COMSOL Multiphysics CUDA Codebeamer Codecov CoppeliaSim Devin Domino Enterprise MLOps Platform Flower Fuzzball Intel Tiber AI Studio JMP Statistical Software Mayhem Code Security NVIDIA TensorRT OpenCV OptSim Overleaf RunMat TPT Tronis Visplore Show More Integrations View All 12 Integrations
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