Order-Embeddings of Images and Language
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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
- University of Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 1 March 2016
- Authors
- Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image captioning
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
- tokens
We use the data splits of Karpathy & Li (2015) for training (113,287 images), validation (5000 images), and test (5000 images).
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
https://github.com/ivendrov/order-embedding Apache License 2.0
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Order-Embeddings of Images and Language
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Order-Embeddings of Images and Language was published by University of Toronto, in Canada, in March 2016. The organisation is categorised as academia.
It works in Vision, and is recorded as doing image captioning.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
Order-Embeddings of Images and Language — common questions
How many parameters does Order-Embeddings of Images and Language have?
No parameter count has been published for Order-Embeddings of Images and Language, which is why no memory or speed figure appears on this page.
Who created Order-Embeddings of Images and Language?
Order-Embeddings of Images and Language was published by University of Toronto, based in Canada, categorised as academia.
When was Order-Embeddings of Images and Language released?
Order-Embeddings of Images and Language 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.
What is Order-Embeddings of Images and Language used for?
Order-Embeddings of Images and Language works in Vision, and is recorded as handling image captioning. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download Order-Embeddings of Images and Language?
The weights for Order-Embeddings of Images and Language are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run Order-Embeddings of Images and Language?
We cannot say. Order-Embeddings of Images and Language has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is Order-Embeddings of Images and Language open source?
Its weights are published, so Order-Embeddings of Images and Language can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
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.