High Performance CNN (NORB)
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
- IDSIA,SUPSI
- Organisation type
- Academia,Academia
- Country
- Switzerland
- Published
- 16 July 2011
- Authors
- Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jürgen Schmidhuber
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Approach
- Supervised
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
- 4.9M
- Training data
- 50,000 tokens
- Epochs
- 7
L1: 6*300*6*6=64800 L3: 500*600*4*4=4800000 L5: (108/4)*500=13500 Total: 64800+4800000+13500=4878300
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.6 × 10¹⁶ FLOP
- How it was established
- Hardware
7 epochs, 35 min per epoch 2 GTX 480 + 2 GTX 580 580 FLOP: 1580000000000 480 FLOP: 1345000000000 FLOP: 7*35*60*(2*1580000000000+2*1345000000000)*0.3=25798500000000000=2.6e16
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 480,NVIDIA GeForce GTX 580
- Chips used
- 4
- Wall-clock time
- 4 hours
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
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
- Historical significance,SOTA improvement
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Flexible, High Performance Convolutional Neural Networks for Image Classification
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
High Performance CNN (NORB) was published by IDSIA,SUPSI, in the country recorded as Switzerland, during July 2011. The publishing organisation is categorised as academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 2.6 × 10¹⁶ FLOP, on hardware recorded as NVIDIA GeForce GTX 480,NVIDIA GeForce GTX 580. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 50,000 tokens of text.
Its inclusion criterion: historical significance,SOTA improvement.
Answers
High Performance CNN (NORB) — common questions
High Performance CNN (NORB)— 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.
High Performance CNN (NORB)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
High Performance CNN (NORB)— how many parameters does it have?
It has a parameter count of 4.9M. L1: 6*300*6*6=64800 L3: 500*600*4*4=4800000 L5: (108/4)*500=13500 Total: 64800+4800000+13500=4878300. 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.
High Performance CNN (NORB)— who created it?
It was published by IDSIA,SUPSI, based in Switzerland, an organisation categorised as academia,Academia.
High Performance CNN (NORB)— when was it released?
It was published in July 2011. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
High Performance CNN (NORB)— 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.
High Performance CNN (NORB)— how much compute was used to train it?
Training consumed around 2.6 × 10¹⁶ FLOP, on hardware recorded as NVIDIA GeForce GTX 480,NVIDIA GeForce GTX 580. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
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.