Mid-level Features
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
- INRIA,Ecole Normale Supèrieure,New York University (NYU)
- Organisation type
- Academia,Academia,Academia
- Country
- France, United States of America
- Published
- 13 June 2010
- Authors
- YL Boureau, F Bach, Y LeCun, J Ponce
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object 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.
- Training data
- 1,500 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1,314
Sources
Where this record came from and when it was last checked.
- Reference
- Learning mid-level features for recognition
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Mid-level Features was published by INRIA,Ecole Normale Supèrieure,New York University (NYU), in France, in June 2010. academia,Academia,Academia is the category the publisher falls under.
It works in Vision, and is recorded as doing object recognition.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
It was trained on about 1,500 tokens of text.
Answers
Mid-level Features — common questions
What is Mid-level Features used for?
Mid-level Features works in Vision, and is recorded as handling object recognition. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Mid-level Features?
None. Mid-level Features 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 Mid-level Features open source?
The licensing for Mid-level Features was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Mid-level Features have?
No parameter count has been published for Mid-level Features, which is why no memory or speed figure appears on this page.
Who created Mid-level Features?
Mid-level Features was published by INRIA,Ecole Normale Supèrieure,New York University (NYU), based in France, categorised as academia,Academia,Academia.
When was Mid-level Features released?
Mid-level Features was published in June 2010. 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.