Dropout (CIFAR)
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
- University of Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 3 June 2012
- Authors
- GE Hinton, N Srivastava, A Krizhevsky
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Character recognition (OCR)
- 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.
- Training data
- 60,000 tokens
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
- 4.3 × 10¹⁵ FLOP
- How it was established
- Hardware
"a single NVIDIA GTX 580 GPU. Training on CIFAR-10 takes roughly 90 minutes" p17 1.581 TFLOP/s * 90 min * 60 s/min * 0.5 utilization
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
- Wall-clock time
- 2 hours
90 minutes
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
- Open (non-commercial)
http://www.cs.toronto.edu/~nitish/dropout see model files
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
- Confident
- Citations
- 7,999
Sources
Where this record came from and when it was last checked.
- Reference
- Improving neural networks by preventing co-adaptation of feature detectors
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Dropout (CIFAR) was published by University of Toronto, in Canada, in June 2012. academia is the category the publisher falls under.
It works in Vision, and is recorded as doing character recognition (OCR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 4.3 × 10¹⁵ FLOP of arithmetic, on NVIDIA GeForce GTX 580, which is a statement about the training budget rather than about inference.
The training set ran to roughly 60,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Dropout (CIFAR) — common questions
What GPU do I need to run Dropout (CIFAR)?
None. Dropout (CIFAR) 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 Dropout (CIFAR) open source?
No. Dropout (CIFAR) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Dropout (CIFAR) have?
No parameter count has been published for Dropout (CIFAR), which is why no memory or speed figure appears on this page.
Who created Dropout (CIFAR)?
Dropout (CIFAR) was published by University of Toronto, based in Canada, categorised as academia.
When was Dropout (CIFAR) released?
Dropout (CIFAR) was published in June 2012. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Dropout (CIFAR) used for?
Dropout (CIFAR) works in Vision, and is recorded as handling character recognition (OCR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Dropout (CIFAR)?
Around 4.3 × 10¹⁵ FLOP, on 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.