Deep LSTM video classifier
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
- University of Texas at Austin,Google
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 1 May 2015
- Authors
- Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Video
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
- 264,000,000 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
- 2,417
Sources
Where this record came from and when it was last checked.
- Reference
- Beyond Short Snippets: Deep Networks for Video Classification
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
Deep LSTM video classifier was published by University of Texas at Austin,Google, in United States of America, in May 2015. The organisation is categorised as academia,Industry.
It works in Video, and is recorded as doing video.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training set ran to roughly 264,000,000 tokens.
Answers
Deep LSTM video classifier — common questions
What is Deep LSTM video classifier used for?
Deep LSTM video classifier works in Video, and is recorded as handling video. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Deep LSTM video classifier?
None. Deep LSTM video classifier 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 Deep LSTM video classifier open source?
The licensing for Deep LSTM video classifier 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 Deep LSTM video classifier have?
No parameter count has been published for Deep LSTM video classifier, which is why no memory or speed figure appears on this page.
Who created Deep LSTM video classifier?
Deep LSTM video classifier was published by University of Texas at Austin,Google, based in United States of America, categorised as academia,Industry.
When was Deep LSTM video classifier released?
Deep LSTM video classifier was published in May 2015. 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.