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Vision AI models
Browse the complete list of Vision AI models. Compare specifications, benchmark scores and provider pricing on GenAIList.
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
Sparse digit recognition SVM
🇩🇪 University of Lubeck
Multiscale deformable part model
🇺🇸 UC Irvine
BLSTM for handwriting (1)
🇩🇪 University of Bern
Fisher Kernel GMM
🇺🇸 Xerox
Local Binary Patterns for facial recognition
🇫🇮 University of Oulu
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
TFE SVM
🇫🇷 Centre de Recherche en Automatique de Nancy (CRAN)
Monocular Depth Prediction
🇺🇸 Stanford University
LMICA
Unknown
Synergistic Face Detector
🇺🇸 NEC Laboratories
Invariant CNN
🇺🇸 New York University (NYU)
GPU implementation of neural networks
🇰🇷 Soongsil University
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
PoE MNIST
🇨🇳 University College London (UCL)
Credibilty Network
🇨🇳 University College London (UCL)
LeNet-5
🇺🇸 AT&T
SOM-CNN
Unknown
AdaBoost.M2 Digit Recognition
🇺🇸 AT&T
System 11
🇺🇸 Carnegie Mellon University (CMU)
MUSIC perceptron
Unknown
LISSOM
🇺🇸 University of Texas at Austin
JPMAX
Unknown
Mixture of linear models
Unknown
ANN Eye Tracker
Unknown
Learning-curve prediction
🇺🇸 AT&T
Siamese-TDNN
🇺🇸 Bell Laboratories
Boosting
🇺🇸 Bell Laboratories
ISR network
🇺🇸 Stanford University
SexNet classification
Unknown
SexNet compression
Unknown
Zip CNN
🇺🇸 AT&T
Handwritten digit recognition network
🇺🇸 AT&T
Invariant image recognition
🇪🇸 Complutense University of Madrid
MLP baggage detector
🇺🇸 Science Applications International Corporation / SAIC
Optimized Multi-Scale Edge Detection
🇺🇸 Massachusetts Institute of Technology (MIT)
Neocognitron
🇯🇵 NHK Broadcasting Science Research Laboratories
Transfer Learning
🇸🇬 University of Zagreb
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.