Fast R-CNN
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
- Microsoft Research
- Organisation type
- Industry
- Country
- United States of America
- Published
- 30 April 2015
- Authors
- R Girshick
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
- Numerical format
- FP32
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.
- Training data
- 25,600,000 tokens
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
- Record confidence
- Unknown
- Citations
- 28,324
Sources
Where this record came from and when it was last checked.
- Reference
- Fast R-CNN
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Fast R-CNN was published by Microsoft Research, in United States of America, in April 2015. The organisation is categorised as industry.
It works in Vision, and is recorded as doing object detection.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
The training set ran to roughly 25,600,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Fast R-CNN — common questions
When was Fast R-CNN released?
Fast R-CNN was published in April 2015. 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 Fast R-CNN used for?
Fast R-CNN works in Vision, and is recorded as handling object detection. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Fast R-CNN?
None. Fast R-CNN 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.
Is Fast R-CNN open source?
The licensing for Fast R-CNN 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 Fast R-CNN have?
No parameter count has been published for Fast R-CNN, which is why no memory or speed figure appears on this page.
Who created Fast R-CNN?
Fast R-CNN was published by Microsoft Research, based in United States of America, categorised as industry.
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.