Low-Cost Collaborative Network
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
- National University of Singapore,University of Technology Sydney,Qihoo 360 AI Institute
- Organisation type
- Academia,Academia,Industry
- Country
- Singapore, Australia, China
- Published
- 15 May 2017
- Authors
- Xuanyi Dong, Junshi Huang, Yi Yang, and Shuicheng Yan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
- 1,280,000 tokens
- Epochs
- 400
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 298
Sources
Where this record came from and when it was last checked.
- Reference
- More is Less: A More Complicated Network with Less Inference Complexity
- Last updated
- 25 May 2026
What the numbers mean
About this model
Low-Cost Collaborative Network was published by National University of Singapore,University of Technology Sydney,Qihoo 360 AI Institute, in the country recorded as Singapore, during May 2017. The category the publisher falls under is academia,Academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 1,280,000 tokens of text.
Answers
Low-Cost Collaborative Network — common questions
Low-Cost Collaborative Network— 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.
Low-Cost Collaborative Network— 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.
Low-Cost Collaborative Network— who created it?
It was published by National University of Singapore,University of Technology Sydney,Qihoo 360 AI Institute, based in Singapore, an organisation categorised as academia,Academia,Industry.
Low-Cost Collaborative Network— when was it released?
It was published in May 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.
Low-Cost Collaborative Network— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Low-Cost Collaborative Network— 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.
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.