PSPNet
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
- Chinese University of Hong Kong (CUHK)
- Organisation type
- Academia
- Country
- Hong Kong
- Published
- 21 July 2017
- Authors
- Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image segmentation
- 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
- tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 13,377
Sources
Where this record came from and when it was last checked.
- Reference
- Pyramid Scene Parsing Network
- Last updated
- 1 January 2026
What the numbers mean
Background
PSPNet was published by Chinese University of Hong Kong (CUHK), in Hong Kong, in July 2017. It comes out of academia.
It works in Vision, and is recorded as doing image segmentation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Its inclusion criterion is highly cited.
Answers
PSPNet — common questions
Who created PSPNet?
PSPNet was published by Chinese University of Hong Kong (CUHK), based in Hong Kong, categorised as academia.
When was PSPNet released?
PSPNet was published in July 2017. 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 PSPNet used for?
PSPNet works in Vision, and is recorded as handling image segmentation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run PSPNet?
None. PSPNet 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 PSPNet open source?
The licensing for PSPNet 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 PSPNet have?
No parameter count has been published for PSPNet, which is why no memory or speed figure appears on this page.
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.