TSN
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
- ETH Zurich,Shenzhen Institute of Advanced Technology,Chinese University of Hong Kong (CUHK)
- Organisation type
- Academia,Academia
- Country
- Switzerland, China, Hong Kong
- Published
- 17 September 2016
- Authors
- Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc Van Gool
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Action recognition
- Numerical format
- FP32
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
- 9,240 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
- 4,113
Sources
Where this record came from and when it was last checked.
- Reference
- Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
- Last updated
- 1 January 2026
What the numbers mean
About this model
TSN was published by ETH Zurich,Shenzhen Institute of Advanced Technology,Chinese University of Hong Kong (CUHK), in the country recorded as Switzerland, during September 2016. The category the publisher falls under is academia,Academia.
It works in the domain of Video, and is recorded as performing the task of action recognition.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training set ran to roughly 9,240 tokens of text.
Answers
TSN — common questions
TSN— 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.
TSN— 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.
TSN— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
TSN— who created it?
It was published by ETH Zurich,Shenzhen Institute of Advanced Technology,Chinese University of Hong Kong (CUHK), based in Switzerland, an organisation categorised as academia,Academia.
TSN— when was it released?
It was published in September 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.
TSN— what is it used for?
It works in the domain of Video, and is recorded as handling the task of action recognition. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.