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Vision AI models
Browse the complete list of Vision AI models. Compare specifications, benchmark scores and provider pricing on GenAIList.
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
DnCNN
🇨🇳 Harbin Institute of Technology
OR-WideResNet
🇺🇸 Duke University
EnhanceNet
🇨🇳 Max Planck Institute for Intelligent Systems
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
Elastic weight consolidation
🇬🇧 DeepMind
Image-to-image cGAN
🇺🇸 University of California (UC) Berkeley
RefineNet
🇦🇺 University of Adelaide
PolyNet
🇺🇸 Chinese University of Hong Kong (CUHK)
DAC-CSR
🇨🇳 Jiangnan University
ResNeXt-101 (64×4d)
🇺🇸 University of California San Diego
ResNeXt-50
🇺🇸 University of California San Diego
Deeply-recursive ConvNet
🇺🇸 Seoul National University
DTN (Domain Transfer Network)
🇺🇸 Facebook AI Research
DLDL (PASCAL)
🇺🇸 University of Oxford
NASv3 (CIFAR-10)
🇺🇸 Google Brain
GAWWN
🇺🇸 University of Michigan
Xception
Wide Residual Network
🇫🇷 Université Paris-Est
MS-CNN
🇺🇸 IBM
ResNet-200
🇺🇸 Microsoft Research Asia
Stacked hourglass network
🇺🇸 University of Michigan
Multi-task Cascaded CNN
🇨🇳 Chinese Academy of Sciences
DenseNet-264
🇨🇳 Tsinghua University
SimpleNet
🇫🇷 Sensifai
Attend-Infer-Repeat
🇺🇸 Google DeepMind
Order embeddings with layer norm
🇺🇸 University of Toronto
CCL
🇨🇳 SenseTime
R-FCN
🇨🇳 Tsinghua University
CMS-RCNN
IEEE
PixelCNN
🇺🇸 Google DeepMind
LRR-4X
🇺🇸 UC Irvine
Symmetric Residual Encoder-Decoder Net
🇺🇸 Nanjing University
Binarized Neural Network (MNIST)
🇺🇸 Technion - Israel Institute of Technology
Template Adaptation
🇺🇸 University of Oxford
Order-Embeddings of Images and Language
🇺🇸 University of Toronto
Convolutional Pose Machines
🇺🇸 Carnegie Mellon University (CMU)
ResNet-101 (ImageNet)
🇺🇸 Microsoft
ResNet-152 (ImageNet)
🇺🇸 Microsoft
SSD
Unknown
Inception v3
3DDFA
🇨🇳 Chinese Academy of Sciences
Highway Network
🇨🇭 IDSIA
Multi-scale Dilated CNN
🇺🇸 Princeton University
SAF R-CNN
🇨🇳 Beijing Institute of Technology
DCNN
🇺🇸 University of Maryland
Deep CNN + COTS
IEEE
CompACT-Deep
🇺🇸 University of California San Diego
BatchNorm
CFSS
🇨🇳 SenseTime
Faster R-CNN
🇺🇸 Microsoft Research
U-Net
🇩🇪 University of Freiburg
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.