RetinaNet-R50
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 7 August 2017
- Authors
- TY Lin, P Goyal, R Girshick, K He
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 34M
- Training data
- 11,500,000,000 tokens
source: table 2 in https://arxiv.org/pdf/1911.09070.pdf
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Citations
- 16,437
Sources
Where this record came from and when it was last checked.
- Reference
- Focal loss for dense object detection
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
RetinaNet-R50 was published by Facebook AI Research, in United States of America, in August 2017. It comes out of industry.
It works in Vision, and is recorded as doing object detection.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training set ran to roughly 11,500,000,000 tokens.
The reason it appears in this catalogue at all is highly cited.
Answers
RetinaNet-R50 — common questions
Is RetinaNet-R50 open source?
The licensing for RetinaNet-R50 was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does RetinaNet-R50 have?
RetinaNet-R50 has 34M parameters. source: table 2 in https://arxiv.org/pdf/1911.09070.pdf. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created RetinaNet-R50?
RetinaNet-R50 was published by Facebook AI Research, based in United States of America, categorised as industry.
When was RetinaNet-R50 released?
RetinaNet-R50 was published in August 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is RetinaNet-R50 used for?
RetinaNet-R50 works in Vision, and is recorded as handling object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run RetinaNet-R50?
None. RetinaNet-R50 is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.