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Vision AI models
Browse the complete list of Vision AI models. Compare specifications, benchmark scores and provider pricing on GenAIList.
Dropout (MNIST)
🇺🇸 University of Toronto
Recursive Neural Network
🇺🇸 Stanford University
Deep Autoencoders
🇺🇸 University of Toronto
Deep rectifier networks
🇺🇸 University of Montreal / Université de Montréal
Optimized Single-layer Net
🇺🇸 University of Michigan
ReLU (LFW)
🇺🇸 University of Toronto
Deconvolutional Network
🇺🇸 New York University (NYU)
Mid-level Features
🇺🇸 INRIA
iCCCP
🇺🇸 Massachusetts Institute of Technology (MIT)
Feedforward NN
🇺🇸 University of Montreal / Université de Montréal
Super-vector coding
🇺🇸 University of Illinois Urbana-Champaign (UIUC)
Two Stage Feature Extraction (MNIST)
🇺🇸 New York University (NYU)
ConvNet Processor
🇺🇸 Courant Institute of Mathematical Sciences
Conv-DBN
🇺🇸 Stanford University
RBM Image Classifier
🇺🇸 University of Toronto
Long-Range Autonomous Off-Road Driving System
🇺🇸 Courant Institute of Mathematical Sciences
Multiscale deformable part model
🇺🇸 UC Irvine
Fisher Kernel GMM
🇺🇸 Xerox
Sparse Vision Encoding
🇺🇸 Stanford University
Hybrid CNN/SVM Object Categorizer
🇺🇸 Courant Institute of Mathematical Sciences
SVM-CNN
🇺🇸 New York University (NYU)
Spatial Pyramid Matching
🇺🇸 INRIA
Monocular Depth Prediction
🇺🇸 Stanford University
Synergistic Face Detector
🇺🇸 NEC Laboratories
Invariant CNN
🇺🇸 New York University (NYU)
Bayesian object categorizer
🇺🇸 California Institute of Technology
Statistical Shape Constellations
🇺🇸 California Institute of Technology
Decision tree (classification)
🇺🇸 Mitsubishi Electric Research Labs
Restricted Boltzmann machine for Face Recognition
🇺🇸 University of Toronto
LeNet-5
🇺🇸 AT&T
AdaBoost.M2 Digit Recognition
🇺🇸 AT&T
System 11
🇺🇸 Carnegie Mellon University (CMU)
LISSOM
🇺🇸 University of Texas at Austin
Learning-curve prediction
🇺🇸 AT&T
Siamese-TDNN
🇺🇸 Bell Laboratories
Boosting
🇺🇸 Bell Laboratories
ISR network
🇺🇸 Stanford University
Zip CNN
🇺🇸 AT&T
Handwritten digit recognition network
🇺🇸 AT&T
MLP baggage detector
🇺🇸 Science Applications International Corporation / SAIC
Optimized Multi-Scale Edge Detection
🇺🇸 Massachusetts Institute of Technology (MIT)
Piecewise linear model
🇺🇸 University of Kansas
Graph-based structural reasoning
🇺🇸 Massachusetts Institute of Technology (MIT)
Print Recognition Logic
🇺🇸 IBM
ADALINE
🇺🇸 Stanford University
Perceptron (1960)
🇺🇸 Cornell Aeronautical Laboratory
Sequence-based pattern recognition
🇺🇸 Massachusetts Institute of Technology (MIT)
About Vision AI models
This page lists every Vision AI models tracked on GenAIList. When choosing a model, weigh raw capability against practical constraints like context window, latency, licensing and price. Open-weights and open-source models can be self-hosted and fine-tuned, while proprietary models often lead on raw quality. Compare benchmark scores on our benchmarks page and put two candidates head to head with compare.
Frequently asked questions
How do I choose the right model?
Weigh raw capability against practical constraints like context window, latency, licensing and price. Use the benchmarks page to compare rankings and the compare tool to evaluate two candidates side by side.
Where can I see benchmark scores?
Visit the benchmarks page to compare these models on standardised tests, then use the compare tool for a detailed side-by-side of any two models.