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Vision AI models
Browse the complete list of Vision AI models. Compare specifications, benchmark scores and provider pricing on GenAIList.
Fast R-CNN
🇺🇸 Microsoft Research
CRF-RNN
🇺🇸 University of Oxford
MSRA (C, PReLU)
🇺🇸 Microsoft Research
VGG-Face
🇺🇸 University of Oxford
ADAM (CIFAR-10)
🇺🇸 University of Amsterdam
DeepLab
Fractional Max-Pooling
🇺🇸 University of Warwick
TA-CNN
🇺🇸 Chinese University of Hong Kong (CUHK)
Cascaded LNet-ANet
🇺🇸 Chinese University of Hong Kong (CUHK)
Fully Convolutional Networks
🇺🇸 University of California (UC) Berkeley
Spatially-Sparse CNN
🇺🇸 University of Warwick
GoogLeNet / InceptionV1
VGG16
🇺🇸 University of Oxford
VGG19
🇺🇸 University of Oxford
NPD
IEEE
ACF-WIDER
🇨🇳 Chinese Academy of Sciences
DeepFace
🇺🇸 Tel Aviv University
Fragment embedding
🇺🇸 Stanford University
SPPNet
🇺🇸 Microsoft
Dropout: SVHN
🇺🇸 University of Toronto
OverFeat
🇺🇸 New York University (NYU)
Image generation
🇺🇸 University of Amsterdam
Network in Network
🇨🇳 National University of Singapore
DeViSE
Visualizing CNNs
🇺🇸 New York University (NYU)
Mitosis
🇨🇭 IDSIA
Hierarchical Scene Labeling (Stanford Background)
🇺🇸 New York University (NYU)
Fisher Vector image classifier
🇫🇷 Universidad Nacional de Cordoba
Maxout Networks
🇺🇸 University of Montreal / Université de Montréal
Textual Imager
🇺🇸 Stanford University
DNN EM segmentation
🇨🇭 IDSIA
DistBelief Vision
AlexNet
🇺🇸 University of Toronto
Unsupervised High-level Feature Learner
Dropout (CIFAR)
🇺🇸 University of Toronto
Dropout (ImageNet)
🇺🇸 University of Toronto
Dropout (MNIST)
🇺🇸 University of Toronto
MCDNN (MNIST)
🇨🇭 IDSIA
CNN committee (traffic sign)
🇨🇭 IDSIA
CNN Committee (MNIST)
🇨🇭 IDSIA
CNN Committee (NIST)
🇨🇭 IDSIA
High Performance CNN (NORB)
🇨🇭 IDSIA
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
Pooling CNN (Caltech 101)
🇩🇪 University of Bonn
Pooling CNN (NORB)
🇩🇪 University of Bonn
Fisher-Boost
🇫🇷 Xerox Research Centre Europe (XRCE)
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
6-layer MLP (MNIST)
🇨🇭 IDSIA
Super-vector coding
🇺🇸 University of Illinois Urbana-Champaign (UIUC)
LCNP LabelMe
🇩🇪 University of Bonn
LCNP MNIST
Unknown
LCNP NORB
Unknown
Two Stage Feature Extraction (MNIST)
🇺🇸 New York University (NYU)
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.