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Vision AI models
Browse the complete list of Vision AI models. Compare specifications, benchmark scores and provider pricing on GenAIList.
ViT-G/14 (LiT)
🇺🇸 Google Research
Masked Autoencoders ViT-H
🇺🇸 Facebook AI Research
DALL-E mini
🇺🇸 Craiyon
SEER
🇺🇸 Facebook AI Research
EfficientNetV2-XL
Denoising Diffusion Probabilistic Models (LSUN Bedroom)
🇺🇸 University of California (UC) Berkeley
CoAtNet
ViT-G/14
🇺🇸 Google Brain
Transformer local-attention (NesT-B)
🇺🇸 Google Cloud
ViT + DINO
🇺🇸 INRIA
ResNet-RS
🇺🇸 Google Brain
Meta Pseudo Labels
🇺🇸 Google Brain
DeiT-B
🇺🇸 Meta AI
HiPPO-LegS
🇺🇸 Stanford University
ViT-Huge/14
🇺🇸 Google Brain
iGPT-XL
🇺🇸 OpenAI
DETR
Once for All
🇺🇸 MIT-IBM Watson AI Lab
Cube-Space AutoEncoder
🇺🇸 MIT-IBM Watson AI Lab
Big Transfer (BiT-M)
🇺🇸 Google Brain
Noisy Student (L2)
🇺🇸 Carnegie Mellon University (CMU)
AlphaX-1
🇺🇸 Facebook AI Research
ResNet-152 + ObjectNet
🇺🇸 Massachusetts Institute of Technology (MIT)
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST
🇺🇸 West Virginia University
LaNet-L (CIFAR-10)
🇺🇸 Brown University
MnasNet-A1 + SSDLite
MnasNet-A3
EfficientNet-B1
Neuro-Symbolic Concept Learner
🇺🇸 Massachusetts Institute of Technology (MIT)
ProxylessNAS
🇺🇸 Massachusetts Institute of Technology (MIT)
Vine copula (breast cancer)
🇺🇸 Massachusetts Institute of Technology (MIT)
ESRGAN
🇺🇸 Chinese University of Hong Kong (CUHK)
Big-Little Net
🇺🇸 IBM
Big-Little Net (vision)
🇺🇸 IBM
ResNeXt-101 32x48d
Diffractive Deep Neural Network
🇺🇸 University of California Los Angeles (UCLA)
YOLOv3
🇺🇸 University of Washington
Residual Dense Network
🇺🇸 Northeastern University
TCN (P-MNIST)
🇺🇸 Carnegie Mellon University (CMU)
DeepLabV3+
AmoebaNet-A (F=448)
🇺🇸 Google Brain
PNASNet-5
🇺🇸 Johns Hopkins University
ProgressiveGAN
🇺🇸 NVIDIA
RetinaNet-R101
🇺🇸 Facebook AI Research
PSPNet
🇺🇸 Chinese University of Hong Kong (CUHK)
JFT
🇺🇸 Google Research
DeepLabV3
EDSR
🇺🇸 Seoul National University
SRGAN
DeepLab (2017)
🇺🇸 Johns Hopkins University
Mask R-CNN
🇺🇸 Facebook AI Research
AlexNet + coordinating filters
🇺🇸 University of Pittsburgh
Prototypical networks
🇺🇸 University of Toronto
Variational Lossy Autoencoder (VLAE) MNIST
🇺🇸 University of California (UC) Berkeley
OR-WideResNet
🇺🇸 Duke University
3DMM-CNN
🇺🇸 University of Southern California
Diabetic Retinopathy Detection Net
🇺🇸 UT Austin
HR-ResNet101
🇺🇸 Carnegie Mellon University (CMU)
GAN-Advancer
🇺🇸 OpenAI
Layer-Norm Fast Weights RNN
🇺🇸 University of Toronto
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.