DANet
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
- Chinese Academy of Sciences
- Organisation type
- Academia
- Country
- China
- Published
- 21 April 2019
- Authors
- Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, Hanqing Lu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Semantic segmentation
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
- 1,757,085,696 tokens
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
MIT for code and weights: https://github.com/junfu1115/DANet/ train code: https://github.com/junfu1115/DANet/tree/master/experiments/recognition
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
- 5,741
Sources
Where this record came from and when it was last checked.
- Reference
- Dual Attention Network for Scene Segmentation
- Last updated
- 1 January 2026
What the numbers mean
About this model
DANet was published by Chinese Academy of Sciences, in China, in April 2019. academia is the category the publisher falls under.
It works in Vision, and is recorded as doing semantic segmentation.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
How it was trained
Around 1,757,085,696 tokens went into training it.
Answers
DANet — common questions
When was DANet released?
DANet was published in April 2019. 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 DANet used for?
DANet works in Vision, and is recorded as handling semantic segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download DANet?
The weights for DANet 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 DANet?
We cannot say. DANet 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 DANet open source?
Its weights are published, so DANet 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.
How many parameters does DANet have?
No parameter count has been published for DANet, which is why no memory or speed figure appears on this page.
Who created DANet?
DANet was published by Chinese Academy of Sciences, based in China, categorised as academia.
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.