MatConvNet vs. Python vs. RunMat vs. VisionPro Deep Learning Comparison Chart

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.

RunMat (by Dystr) is a fast, free, open-source alternative for running MATLAB code. Users can run their existing MATLAB code with complete language grammar and core semantics. No license fees, no lock-in. RunMat is built with a modern compiler, which enables blazing-fast calculations. It boots in 5 milliseconds, GPU optimization is enabled by default, and it's a single, compact, cross-platform binary. Typical engineering use cases - Controls/signal processing & numerics: accelerate MATLAB-style loops plus heavy linear algebra; enjoy faster iteration due to instant startup and tiered JIT. - Batch/CI & serverless jobs: snapshots + compact binaries make it easy to run .m workloads in containers or ephemeral runners at scale. - Plot-heavy workflows: interactive GPU plots for exploratory analysis and reportable exports for stakeholders. - Education: remove license friction and start labs instantly; Jupyter kernel supports reproducible worksheets.