DrLIM
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
- 17 June 2006
- Authors
- R. Hadsell; S. Chopra; Y. LeCun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image embedding
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
- 37.1K
- Training data
- 36,060,000 tokens
Architecture described in figure 3
"The dataset was split into 660 training images and a 312 test images. The result of training on all 10989 similar pairs and 206481 dissimilar pairs is a 3-dimensional manifold in the shape of a cylinder (see figure 8)." 206481 + 10989 = 217470
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 4,888
Sources
Where this record came from and when it was last checked.
- Reference
- Dimensionality Reduction by Learning an Invariant Mapping
- Last updated
- 28 November 2025
What the numbers mean
Background
DrLIM was published by New York University (NYU), in the country recorded as United States of America, during June 2006. The publishing organisation is categorised as academia.
It works in the domain of Other, and is recorded as performing the task of image embedding.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training consumed a corpus of around 36,060,000 tokens of text.
Answers
DrLIM — common questions
DrLIM— 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.
DrLIM— 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.
DrLIM— how many parameters does it have?
It has a parameter count of 37.1K. Architecture described in figure 3. 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.
DrLIM— who created it?
It was published by New York University (NYU), based in United States of America, an organisation categorised as academia.
DrLIM— when was it released?
It was published in June 2006. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DrLIM— what is it used for?
It works in the domain of Other, and is recorded as handling the task of image embedding. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.