Open-weight vision models in 2016
9
Models
8 organisations
100%
Open weights
9 open, 0 closed
0
Frontier
By Epoch's flag
83M
Largest traceable
ResNeXt-101 (64×4d)
2016
Span
5 with a traceable size
Releases
by monthOrganisation
Records
9| Model | Organisation | Released | Parameters | Weights |
|---|---|---|---|---|
| 3DMM-CNN | University of Southern California | 2016-12 | 45M | Open |
| EnhanceNet | Max Planck Institute for Intelligent Systems | 2016-12 | 814,464 | Open |
| HR-ResNet101 | Carnegie Mellon University (CMU) | 2016-12 | 45M | Open |
| YOLOv2 | University of Washington | 2016-12 | 51M * | Open |
| DLDL (PASCAL) | University of Oxford | 2016-11 | 564M * | Open |
| ResNeXt-101 (64×4d) | University of California San Diego | 2016-11 | 83M | Open |
| ResNeXt-50 | University of California San Diego | 2016-11 | 25M * | Open |
| DenseNet-264 | Tsinghua University | 2016-08 | 34M | Open |
| LRR-4X | UC Irvine | 2016-05 | 138M * | Open |
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.