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 Germany, in November 2017. academia is the category the publisher falls under.
It works in Video, and is recorded as doing 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
Around 509,914 tokens went into training it.
Its inclusion criterion is highly cited,Historical significance.
Answers
TriNet — common questions
What is TriNet used for?
TriNet works in Video, and is recorded as handling 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.
What GPU do I need to run TriNet?
None. TriNet 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 TriNet open source?
The licensing for TriNet 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 TriNet have?
No parameter count has been published for TriNet, which is why no memory or speed figure appears on this page.
Who created TriNet?
TriNet was published by Visual Computing Institute,RWTH Aachen University, based in Germany, categorised as academia.
When was TriNet released?
TriNet 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.