SimpleNet
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
- Sensifai,Islamic Azad University,Technicolor R&I,Institute for Research in Fundamental Sciences (IPM)
- Organisation type
- Industry,Academia
- Country
- Belgium, Iran (Islamic Republic of), France
- Published
- 22 August 2016
- Authors
- Seyyed Hossein Hasanpour, Mohammad Rouhani, Mohsen Fayyaz, Mohammad Sabokrou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- 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.
- Parameters
- 5.5M
- Training data
- 1,280,000 tokens
SOTA CIFAR-10 model was 5.48m params
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX 980
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
- SOTA improvement
- Record confidence
- Confident
- Citations
- 128
"We achieved state-of-theart result on CIFAR10 outperforming several heavier architectures"
Sources
Where this record came from and when it was last checked.
- Reference
- Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
SimpleNet was published by Sensifai,Islamic Azad University,Technicolor R&I,Institute for Research in Fundamental Sciences (IPM), in the country recorded as Belgium, during August 2016. The publishing organisation is categorised as industry,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It was trained on a corpus of about 1,280,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
SimpleNet — common questions
SimpleNet— who created it?
It was published by Sensifai,Islamic Azad University,Technicolor R&I,Institute for Research in Fundamental Sciences (IPM), based in Belgium, an organisation categorised as industry,Academia.
SimpleNet— when was it released?
It was published in August 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SimpleNet— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. 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.
SimpleNet— 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.
SimpleNet— 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.
SimpleNet— how many parameters does it have?
It has a parameter count of 5.5M. SOTA CIFAR-10 model was 5.48m params. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.