DNCON2
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
- University of Missouri
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 May 2018
- Authors
- Badri Adhikari, Jie Hou, Jianlin Cheng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Proteins, Protein folding prediction
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
- 444,030,000 tokens
- Epochs
- 1,600
"Our raw feature files for all 1426 training proteins"
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
- 9.5 × 10¹⁶ FLOP
- How it was established
- Hardware
"Our training was conducted on Tesla K20 Nvidia GPUs each having 5 GB of GPU memory, on which, training one model took around 12 h." "We train each CNN for a total of 1600 epochs with each epoch of training taking around 2 min." Assumptions: peakFLOP rate 3.52e12FLOP/s (from: https://www.techpowerup.com/gpu-specs/tesla-k20c.c564) 30% utilization rate 1 GPU Estimate 1: "training one model took around 12h" => unclear how many GPUs (12 *3600) s * 3.52e12 FLOP/s * 0.3 = 4.5e16 FLOP Estimate 2: "…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 12 hours
"training one model took around 12 h"
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
license: https://github.com/multicom-toolbox/DNCON2?tab=GPL-3.0-1-ov-file#readme
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 173
Sources
Where this record came from and when it was last checked.
- Reference
- DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
DNCON2 was published by University of Missouri, in the country recorded as United States of America, during May 2018. It comes out of an organisation categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of proteins, Protein folding prediction.
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
The training run consumed about 9.5 × 10¹⁶ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 444,030,000 tokens of text.
Answers
DNCON2 — common questions
DNCON2— how much compute was used to train it?
Training consumed around 9.5 × 10¹⁶ FLOP. 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.
DNCON2— what GPU do I need to run it?
We cannot say. It 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.
DNCON2— is it open source?
Its weights are published, so it 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.
DNCON2— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
DNCON2— who created it?
It was published by University of Missouri, based in United States of America, an organisation categorised as academia.
DNCON2— when was it released?
It was published in May 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DNCON2— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of proteins, Protein folding prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DNCON2— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.