Cascaded LNet-ANet
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
- Chinese University of Hong Kong (CUHK)
- Organisation type
- Academia
- Country
- Hong Kong
- Published
- 28 November 2014
- Authors
- Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face detection
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
- 9,320,000 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
- 9,584
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Learning Face Attributes in the Wild
- Last updated
- 25 May 2026
What the numbers mean
Background
Cascaded LNet-ANet was published by Chinese University of Hong Kong (CUHK), in the country recorded as Hong Kong, during November 2014. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of face 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 9,320,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
Cascaded LNet-ANet — common questions
Cascaded LNet-ANet— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of face detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Cascaded LNet-ANet— 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.
Cascaded LNet-ANet— 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.
Cascaded LNet-ANet— 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.
Cascaded LNet-ANet— who created it?
It was published by Chinese University of Hong Kong (CUHK), based in Hong Kong, an organisation categorised as academia.
Cascaded LNet-ANet— when was it released?
It was published in November 2014. 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.