DAC-CSR
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
- Jiangnan University,University of Surrey
- Organisation type
- Academia,Academia
- Country
- China, United Kingdom of Great Britain and Northern Ireland
- Published
- 16 November 2016
- Authors
- Zhen-Hua Feng, Josef Kittler, William Christmas, Patrik Huber, Xiao-Jun Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face detection
- Approach
- Supervised
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
- 20,000 tokens
[images] AFLW-full splits the 24386 images into 20000/4386 for training/testing.
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
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
- SOTA improvement
- Record confidence
- Confident
https://paperswithcode.com/sota/face-alignment-on-aflw-19
Sources
Where this record came from and when it was last checked.
- Reference
- Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DAC-CSR was published by Jiangnan University,University of Surrey, in China, in November 2016. The organisation is categorised as academia,Academia.
It works in Vision, and is recorded as doing face detection.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Around 20,000 tokens went into training it.
Its inclusion criterion is sOTA improvement.
Answers
DAC-CSR — common questions
Who created DAC-CSR?
DAC-CSR was published by Jiangnan University,University of Surrey, based in China, categorised as academia,Academia.
When was DAC-CSR released?
DAC-CSR was published in November 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 DAC-CSR used for?
DAC-CSR works in Vision, and is recorded as handling face detection. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run DAC-CSR?
None. DAC-CSR 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 DAC-CSR open source?
No. DAC-CSR has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DAC-CSR have?
No parameter count has been published for DAC-CSR, 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.