Deep CNN + COTS
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
- IEEE
- Organisation type
- Industry
- Country
- Multinational
- Published
- 26 July 2015
- Authors
- Dayong Wang, C. Otto, Anil K. Jain
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face recognition
- 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.
- Parameters
- 5M
- Training data
- 494,414 tokens
Taken from Table 1 of https://arxiv.org/abs/1508.01722 which uses the same architecture
CASIA [6] dataset provides a large collection of labeled (based on subject names) training set for deep learning networks. It contains 494,414 images of 10,575 subjects
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA Tesla K40c
- Chips used
- 1
- Power draw
- 286 W
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
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-verification-on-ijb-a "Experimental results demonstrate that the deep features are competitive with state-of-the-art methods on unconstrained face recognition benchmarks (LFW and IJB-A). "
Sources
Where this record came from and when it was last checked.
- Reference
- Face Search at Scale: 80 Million Gallery
- Last updated
- 28 November 2025
What the numbers mean
Background
Deep CNN + COTS was published by IEEE, in Multinational, in July 2015. industry is the category the publisher falls under.
It works in Vision, and is recorded as doing face recognition.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Around 494,414 tokens went into training it.
Its inclusion criterion is sOTA improvement.
Answers
Deep CNN + COTS — common questions
What is Deep CNN + COTS used for?
Deep CNN + COTS works in Vision, and is recorded as handling face recognition. 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 Deep CNN + COTS?
None. Deep CNN + COTS 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 CNN + COTS open source?
No. Deep CNN + COTS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Deep CNN + COTS have?
Deep CNN + COTS has 5M parameters. Taken from Table 1 of https://arxiv.org/abs/1508.01722 which uses the same architecture. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Deep CNN + COTS?
Deep CNN + COTS was published by IEEE, based in Multinational, categorised as industry.
When was Deep CNN + COTS released?
Deep CNN + COTS was published in July 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.