Unrecorded-weight vision models in 2017
16
Models
13 organisations
—
Open weights
0 open, 0 closed
1
Frontier
By Epoch's flag
53M
Largest traceable
RetinaNet-R101
2017
Span
3 with a traceable size
Releases
by monthOrganisation
Records
16| Model | Organisation | Released | Parameters | Weights |
|---|---|---|---|---|
| ProgressiveGAN | NVIDIA | 2017-10 | — | Not recorded |
| SENet (ImageNet) | Chinese Academy of Sciences | 2017-09 | 28M * | Not recorded |
| RetinaNet-R101 | Facebook AI Research | 2017-08 | 53M | Not recorded |
| RetinaNet-R50 | Facebook AI Research | 2017-08 | 34M * | Not recorded |
| JFT | Google Research | 2017-07 | 45M | Not recorded |
| NASNet-A | Google Brain | 2017-07 | 89M * | Not recorded |
| PSPNet | Chinese University of Hong Kong (CUHK) | 2017-07 | — | Not recorded |
| ShuffleNet v1 | Megvii Inc | 2017-07 | 2M * | Not recorded |
| DeepLabV3 | 2017-06 | — | Not recorded | |
| EDSR | Seoul National University | 2017-06 | — | Not recorded |
| DeepLab (2017) | Johns Hopkins University | 2017-04 | — | Not recorded |
| MobileNet | 2017-04 | 4M * | Not recorded | |
| Mask R-CNN | Facebook AI Research | 2017-03 | — | Not recorded |
| Prototypical networks | University of Toronto | 2017-03 | — | Not recorded |
| DnCNN | Harbin Institute of Technology | 2017-02 | — | Not recorded |
| OR-WideResNet | Duke University | 2017-01 | 18M | 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.