DCNN
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
- University of Maryland,Rutgers University
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 7 August 2015
- Authors
- Jun-Cheng Chen, Vishal M. Patel, Rama Chellappa
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face verification
- 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
- 490,356 tokens
- Epochs
- 26
Table 1
"The CASIA-WebFace dataset contains 494,414 face images of 10,575 subjects downloaded from the IMDB website. After removing the 27 overlapping subjects with the IJB-A dataset, there are 10548 subjects 1 and 490,356 face images."
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 4.8 × 10¹⁷ FLOP
- How it was established
- Operation counting,Hardware
Total compute: 1535436288 forward pass flops *3* 1000000 iterations * 128 batch size = 589607534592000000 FLOP = 5.89607534592e17 FLOP Forward pass FLOPs based on CNN details in Table 1: Conv11: 2*3*3*32*100*100*1=5760000 Conv12: 2*3*3*64*100*100*32=368640000 Conv21: 2*3*3*64*50*50*64=184320000 Conv22: 2*3*3*64*50*50*128=368640000 Conv32: 2*3*3*128*25*25*96=138240000 Conv33: 2*3*3*96*25*25*192=207360000 Conv41: 2*3*3*192*13*13*128=74760192 Conv42: 2*3*3*128*13*13*256=99680256 Conv51: 2*3*3*256*…
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
- Wall-clock time
- 216 hours (9 days)
- Power draw
- 286 W
"The DCNN model is trained for about 9 days using NVidia Tesla K40."
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-verification-on-ijb-a
Sources
Where this record came from and when it was last checked.
- Reference
- Unconstrained Face Verification using Deep CNN Features
- Last updated
- 28 November 2025
What the numbers mean
About this model
DCNN was published by University of Maryland,Rutgers University, in United States of America, in August 2015. The organisation is categorised as academia,Academia.
It works in Vision, and is recorded as doing face verification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training it took roughly 4.8 × 10¹⁷ FLOP of computation, on NVIDIA Tesla K40c — a measure of what producing the model cost, not of how fast it answers.
Around 490,356 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
DCNN — common questions
When was DCNN released?
DCNN was published in August 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.
What is DCNN used for?
DCNN works in Vision, and is recorded as handling face verification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train DCNN?
Around 4.8 × 10¹⁷ FLOP, on NVIDIA Tesla K40c. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run DCNN?
None. DCNN 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 DCNN open source?
No. DCNN has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DCNN have?
DCNN has 5M parameters. Table 1. 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 DCNN?
DCNN was published by University of Maryland,Rutgers University, based in United States of America, categorised as academia,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.