CNN committee (traffic sign)
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
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 3 October 2011
- Authors
- D. Ciresan, U. Meier, Jonathan Masci, J. 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
- 1.4M
- Training data
- 53,280 tokens
- Epochs
- 50
Parameters L1: 3*3*3*100=2700 L3: 100*4*4*150=240000 L5: 150*3*3*250=337500 L7: 250*4*4*200=800000 L8: 200*43=8600 Total: 2700+240000+337500+800000+8600=1388800
[images] “The training set consists of 26640 images“ “we resize all images to 48 × 48 pixels”
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
- 9.9 × 10¹⁴ FLOP
- How it was established
- Operation counting
Training FLOP L1: 3*3*3*46*46*100=5713200 L3: 100*4*4*20*20*150=96000000 L5: 150*3*3*8*8*250=21600000 L7: 250*4*4*200=800000 L8: 200*43=8600 Total: 2*(5713200+96000000+21600000+800000+8600)=248243600 Training Compute: 248243600*3*26640*50=991981425600000=9.9e14
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 580,NVIDIA GeForce GTX 480
- Chips used
- 4
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
"We describe the approach that won the preliminary phase of the German traffic sign recognition benchmark with a better-than-human recognition rate of 98.98%.We obtain an even better recognition rate of 99.15% by further training the nets. "
Sources
Where this record came from and when it was last checked.
- Reference
- A committee of neural networks for traffic sign classification
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
CNN committee (traffic sign) was published by IDSIA, in Switzerland, in October 2011. It comes out of academia.
It works in Vision, and is recorded as doing image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took roughly 9.9 × 10¹⁴ FLOP of computation, on NVIDIA GeForce GTX 580,NVIDIA GeForce GTX 480 — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 53,280 tokens.
The reason it appears in this catalogue at all is historical significance,SOTA improvement.
Answers
CNN committee (traffic sign) — common questions
What is CNN committee (traffic sign) used for?
CNN committee (traffic sign) works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train CNN committee (traffic sign)?
Around 9.9 × 10¹⁴ FLOP, on NVIDIA GeForce GTX 580,NVIDIA GeForce GTX 480. 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.
What GPU do I need to run CNN committee (traffic sign)?
None. CNN committee (traffic sign) 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 CNN committee (traffic sign) open source?
No. CNN committee (traffic sign) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does CNN committee (traffic sign) have?
CNN committee (traffic sign) has 1.4M parameters. Parameters L1: 3*3*3*100=2700 L3: 100*4*4*150=240000 L5: 150*3*3*250=337500 L7: 250*4*4*200=800000 L8: 200*43=8600 Total: 2700+240000+337500+800000+8600=1388800. 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.
Who created CNN committee (traffic sign)?
CNN committee (traffic sign) was published by IDSIA, based in Switzerland, categorised as academia.
When was CNN committee (traffic sign) released?
CNN committee (traffic sign) was published in October 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.
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.