OverFeat
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
- 21 December 2013
- Authors
- Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, 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
- 144M
- Training data
- tokens
- Epochs
- 80
144M (Table 4)
"We then extract 5 random crops (and their horizontal flips) of size 221x221 pixels and present these to the network in mini-batches of size 128."
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 Tesla K20X
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
- 5,148
Sources
Where this record came from and when it was last checked.
- Reference
- OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
OverFeat was published by New York University (NYU), in United States of America, in December 2013. It comes out of academia.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Its inclusion criterion is highly cited.
Answers
OverFeat — common questions
How many parameters does OverFeat have?
OverFeat has 144M parameters. 144M (Table 4). 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 OverFeat?
OverFeat was published by New York University (NYU), based in United States of America, categorised as academia.
When was OverFeat released?
OverFeat was published in December 2013. 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 OverFeat used for?
OverFeat works in Vision, and is recorded as handling image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run OverFeat?
None. OverFeat 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 OverFeat open source?
The licensing for OverFeat was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.