LRSO-GAN
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 Technology Sydney
- Organisation type
- Academia
- Country
- Australia
- Published
- 22 October 2017
- Authors
- Zhedong Zheng, Liang Zheng, Yi Yang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- 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
- 62,808 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 2,034
Sources
Where this record came from and when it was last checked.
- Reference
- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
- Last updated
- 25 May 2026
What the numbers mean
About this model
LRSO-GAN was published by University of Technology Sydney, in Australia, in October 2017. academia is the category the publisher falls under.
It works in Vision, and is recorded as doing person re-identification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Around 62,808 tokens went into training it.
Answers
LRSO-GAN — common questions
When was LRSO-GAN released?
LRSO-GAN was published in October 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 LRSO-GAN used for?
LRSO-GAN works in Vision, and is recorded as handling person re-identification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run LRSO-GAN?
None. LRSO-GAN 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 LRSO-GAN open source?
The licensing for LRSO-GAN 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 LRSO-GAN have?
No parameter count has been published for LRSO-GAN, which is why no memory or speed figure appears on this page.
Who created LRSO-GAN?
LRSO-GAN was published by University of Technology Sydney, based in Australia, categorised as academia.
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.