Dropout: SVHN
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
- 1 June 2014
- Authors
- Nitish Shrivasta, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification, Digit recognition
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
- 47.8M
- Training data
- 600,000 tokens
"The best architecture that we found uses three convolutional layers each followed by a max-pooling layer. The convolutional layers have 96, 128 and 256 filters respectively. Each convolutional layer has a 5 × 5 receptive field applied with a stride of 1 pixel. Each max pooling layer pools 3 × 3 regions at strides of 2 pixels. The convolutional layers are followed by two fully connected hidden layers having 2048 units each." Inputs: 32 x 32 x 3 conv_1: 96 x 5 x 5 x 3 = 7,200 conv_2: 128 x 5 x 5 …
Appendix B.2: "The SVHN data set consists of approximately 600,000 training images and 26,000 test images" dimensionality 3072 (32 × 32 color)
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,SOTA improvement
- Record confidence
- Confident
- Citations
- 41,962
SOTA on the Street View House Numbers dataset
Sources
Where this record came from and when it was last checked.
- Reference
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- Last updated
- 1 January 2026
What the numbers mean
About this model
Dropout: SVHN was published by University of Toronto, in the country recorded as Canada, during June 2014. It comes out of an organisation categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of image classification, Digit recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training set ran to roughly 600,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited,SOTA improvement.
Answers
Dropout: SVHN — common questions
Dropout: SVHN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Dropout: SVHN— how many parameters does it have?
It has a parameter count of 47.8M. "The best architecture that we found uses three convolutional layers each followed by a max-pooling layer. The convolutional layers have 96, 128 and 256 filters respectively. Each convolutional layer has a 5 × 5 receptive field applied with a stride of 1 pixel. Each max pooling layer pools 3 × 3 regions at strides of 2 pixels. The convolutional layers are followed by two fully connected hidden layers having 2048 units each." Inputs: 32 x 32 x 3 conv_1: 96 x 5 x 5 x 3 = 7,200 conv_2: 128 x 5 x 5 x 96 = 307,200 conv_3: 256 x 5 x 5 x 128 = 819,200 (output shape after CNN 3 will be: 20 x 20 x 256) max_pool has no learnable parameters but further reduces output shape to 9 x 9 x 256 FFN_1: (9 x 9 x 256) x 2048 = 42,467,328 FFN_2: 2048 x 2048 = 4,194,304 So total number of parameters appear to be 47,795,232. 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.
Dropout: SVHN— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
Dropout: SVHN— when was it released?
It was published in June 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Dropout: SVHN— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification, Digit recognition. 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.
Dropout: SVHN— 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.