R-CNN (T-net)
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
- University of California (UC) Berkeley
- Organisation type
- Academia
- Country
- United States of America
- Published
- 11 November 2013
- Authors
- Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
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.
- Parameters
- 69M
- Training data
- tokens
Computed from architecture description in Caffee https://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/detection.ipynb
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
- 28,903
Sources
Where this record came from and when it was last checked.
- Reference
- Rich feature hierarchies for accurate object detection and semantic segmentation
- Last updated
- 25 May 2026
What the numbers mean
What this model is
R-CNN (T-net) was published by University of California (UC) Berkeley, in the country recorded as United States of America, during November 2013. It comes out of an organisation categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of object detection.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
R-CNN (T-net) — common questions
R-CNN (T-net)— what GPU do I need to run it?
None. This 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.
R-CNN (T-net)— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
R-CNN (T-net)— how many parameters does it have?
It has a parameter count of 69M. Computed from architecture description in Caffee https://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/detection.ipynb. 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.
R-CNN (T-net)— who created it?
It was published by University of California (UC) Berkeley, based in United States of America, an organisation categorised as academia.
R-CNN (T-net)— when was it released?
It was published in November 2013. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
R-CNN (T-net)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.