The AI Museum
AI Analytics1,072 recordsRecounted 23 September 2026

Unrecorded-weight vision models

Weightsopenclosedunknownfrontier only
Year20262025202420232022202120202019201820172016
2 filters active, showing 113 of 1,072 recordsClear all

113

Models

55 organisations

—

Open weights

0 open, 0 closed

28

Frontier

By Epoch's flag

144M

Largest traceable

OverFeat

1955–2018

Span

33 with a traceable size

Releases

by year, from 2003
2003200420052006200720082009201020112012201320142015201620172018

Organisation

Country

Domain

Records

newest 50 of 113
Unrecorded-weight vision models, newest first. Each row links to the source Epoch AI records for it.
ModelOrganisationReleasedParametersWeights
MobileNetV2Google2018-063M *Not recorded
DeepLabV3+Google2018-02—Not recorded
TCN (P-MNIST)Carnegie Mellon University (CMU)2018-0242,000Not recorded
ProgressiveGANNVIDIA2017-10—Not recorded
SENet (ImageNet)Chinese Academy of Sciences2017-0928M *Not recorded
RetinaNet-R101Facebook AI Research2017-0853MNot recorded
RetinaNet-R50Facebook AI Research2017-0834M *Not recorded
JFTGoogle Research2017-0745MNot recorded
NASNet-AGoogle Brain2017-0789M *Not recorded
PSPNetChinese University of Hong Kong (CUHK)2017-07—Not recorded
ShuffleNet v1Megvii Inc2017-072M *Not recorded
DeepLabV3Google2017-06—Not recorded
EDSRSeoul National University2017-06—Not recorded
DeepLab (2017)Johns Hopkins University2017-04—Not recorded
MobileNetGoogle2017-044M *Not recorded
Mask R-CNNFacebook AI Research2017-03—Not recorded
Prototypical networksUniversity of Toronto2017-03—Not recorded
DnCNNHarbin Institute of Technology2017-02—Not recorded
OR-WideResNetDuke University2017-0118MNot recorded
Elastic weight consolidationDeepMind2016-12—Not recorded
Image-to-image cGANUniversity of California (UC) Berkeley2016-11—Not recorded
NASv3 (CIFAR-10)Google Brain2016-1137M *Not recorded
PolyNetChinese University of Hong Kong (CUHK)2016-1192M *Not recorded
XceptionGoogle2016-1023MNot recorded
ResNet-1001Microsoft2016-0910M *Not recorded
Wide Residual NetworkUniversité Paris-Est2016-09—Not recorded
SimpleNetSensifai2016-085MNot recorded
PixelCNNGoogle DeepMind2016-06—Not recorded
R-FCNTsinghua University2016-06—Not recorded
Inception-ResNet-V2Google2016-0256M *Not recorded
Inceptionv4Google2016-0243M *Not recorded
SqueezeNetDeepScale2016-021M *Not recorded
Inception v3Google2015-1224M *Not recorded
ResNet-110 (CIFAR-10)Microsoft2015-122M *Not recorded
ResNet-152 (ImageNet)Microsoft2015-1260MNot recorded
Multi-scale Dilated CNNPrinceton University2015-11—Not recorded
BatchNormGoogle2015-0614MNot recorded
YOLOUniversity of Washington2015-06272M *Not recorded
Fast R-CNNMicrosoft Research2015-04—Not recorded
MSRA (C, PReLU)Microsoft Research2015-0287MNot recorded
Fractional Max-PoolingUniversity of Warwick2014-1227M *Not recorded
Cascaded LNet-ANetChinese University of Hong Kong (CUHK)2014-11—Not recorded
Fully Convolutional NetworksUniversity of California (UC) Berkeley2014-11—Not recorded
Deeply-supervised netsMicrosoft Research2014-09—Not recorded
GoogLeNet / InceptionV1Google2014-097MNot recorded
Spatially-Sparse CNNUniversity of Warwick2014-09—Not recorded
VGG16University of Oxford2014-09138MNot recorded
VGG19University of Oxford2014-09144M *Not recorded
DeepFaceTel Aviv University2014-06—Not recorded
Fragment embeddingStanford University2014-06144M *Not recorded

An asterisk marks a parameter figure the source does not consider traceable to the people who built the model. That judgement is the source’s and it has false negatives: Kimi K3 is marked speculative at 2.8T while Moonshot’s own model card states the figure. Why that distinction carries the whole exhibit.

Counts are of models Epoch AI records as notable, not of every model released. The most recent month is always incomplete, because a model enters the dataset when it is reviewed rather than when it ships.

Epoch AI, 'Data on AI Models'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/data/ai-models-documentation' Used under CC BY 4.0.

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