TriNet
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
- Visual Computing Institute,RWTH Aachen University
- Organisation type
- Academia
- Country
- Germany
- Published
- 21 November 2017
- Authors
- Alexander Hermans, Lucas Beyer, Bastian Leibe
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Person re-identification
- 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
- 509,914 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,Historical significance
- Record confidence
- Unknown
- Citations
- 3,553
Sources
Where this record came from and when it was last checked.
- Reference
- In Defense of the Triplet Loss for Person Re-Identification
- Last updated
- 25 May 2026
What the numbers mean
Background
TriNet was published by Visual Computing Institute,RWTH Aachen University, in the country recorded as Germany, during November 2017. The category the publisher falls under is academia.
It works in the domain of Video, and is recorded as performing the task of person re-identification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training consumed a corpus of around 509,914 tokens of text.
Its inclusion criterion: highly cited,Historical significance.
Answers
TriNet — common questions
TriNet— what is it used for?
It works in the domain of Video, and is recorded as handling the task of person re-identification. 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.
TriNet— 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.
TriNet— 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.
TriNet— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
TriNet— who created it?
It was published by Visual Computing Institute,RWTH Aachen University, based in Germany, an organisation categorised as academia.
TriNet— when was it released?
It was published in November 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.
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.