Two Stage Feature Extraction (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
- New York University (NYU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 September 2009
- Authors
- Kevin Jarrett, K. Kavukcuoglu, Marc'Aurelio Ranzato, Yann LeCun
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
- 258.8K
- Training data
- 50,000 tokens
- Epochs
- 33
CNN: 32*5*5+32*64*5*5=52000 FC: 1024*200+200*10=206800 Total: 52000+206800=258800
"experiments were run on the MNIST dataset, which contains 60,000 gray-scale 28x28 pixel digit images for training and 10,000 images for testing"
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
- 2.1 × 10¹³ FLOP
- How it was established
- Operation counting
Assuming no padding and stride 1 “on the 50,000 non-validation training samples until the best error rate on the validation set was reached (this took 30 epochs)“ First CNN layer: 2*32*24*24*5*5=921600 Second CNN layer: 2*64*16*5*5*8*8=3276800 First FC layer: 2*4*4*64*200=409600 Second FC layer: 2*200*10=4000 Total forward FLOP: 921600+3276800+409600+4000=4612000 Totral training compute: 4612000*3*30*50000=20754000000000 They train another model in this paper that I think is at least an order o…
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
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
They claim SOTA performance on the MNIST and NORB datasets. Is also highly cited
Sources
Where this record came from and when it was last checked.
- Reference
- What is the best multi-stage architecture for object recognition?
- Last updated
- 11 February 2026
What the numbers mean
About this model
Two Stage Feature Extraction (MNIST) was published by New York University (NYU), in the country recorded as United States of America, during September 2009. The category the publisher falls under is 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.
How it was trained
The training run consumed about 2.1 × 10¹³ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 50,000 tokens of text.
The reason it appears in this catalogue at all: highly cited,SOTA improvement.
Answers
Two Stage Feature Extraction (MNIST) — common questions
Two Stage Feature Extraction (MNIST)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Two Stage Feature Extraction (MNIST)— how many parameters does it have?
It has a parameter count of 258.8K. CNN: 32*5*5+32*64*5*5=52000 FC: 1024*200+200*10=206800 Total: 52000+206800=258800. 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.
Two Stage Feature Extraction (MNIST)— who created it?
It was published by New York University (NYU), based in United States of America, an organisation categorised as academia.
Two Stage Feature Extraction (MNIST)— when was it released?
It was published in September 2009. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Two Stage Feature Extraction (MNIST)— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Two Stage Feature Extraction (MNIST)— how much compute was used to train it?
Training consumed around 2.1 × 10¹³ FLOP. 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.
Two Stage Feature Extraction (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.
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.