Binarized Neural Network (MNIST)
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
- Technion - Israel Institute of Technology,Columbia University,University of Montreal / Université de Montréal
- Organisation type
- Academia,Academia,Academia
- Country
- Israel, United States of America, Canada
- Published
- 17 March 2016
- Authors
- Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
- 37M
- Training data
- 60,000 tokens
- Epochs
- 1,000
Parameter count is not explicitly stated, but they give details: "The MLP we train on MNIST consists of 3 hidden layers of 4096 binary units (see Section 1) and a L2-SVM output layer" Approximately 37m, based on 784 pixels * 4096 + 2 * 4096^2
60k training images, 10k test in MNIST
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 3,299
Sources
Where this record came from and when it was last checked.
- Reference
- Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Binarized Neural Network (MNIST) was published by Technion - Israel Institute of Technology,Columbia University,University of Montreal / Université de Montréal, in the country recorded as Israel, during March 2016. It comes out of an organisation categorised as academia,Academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training set ran to roughly 60,000 tokens of text.
Answers
Binarized Neural Network (MNIST) — common questions
Binarized Neural Network (MNIST)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Binarized Neural Network (MNIST)— 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.
Binarized Neural Network (MNIST)— 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.
Binarized Neural Network (MNIST)— how many parameters does it have?
It has a parameter count of 37M. Parameter count is not explicitly stated, but they give details: "The MLP we train on MNIST consists of 3 hidden layers of 4096 binary units (see Section 1) and a L2-SVM output layer" Approximately 37m, based on 784 pixels * 4096 + 2 * 4096^2. 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.
Binarized Neural Network (MNIST)— who created it?
It was published by Technion - Israel Institute of Technology,Columbia University,University of Montreal / Université de Montréal, based in Israel, an organisation categorised as academia,Academia,Academia.
Binarized Neural Network (MNIST)— when was it released?
It was published in March 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.
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.