DeepLoc
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
- Technical University of Denmark,University of Copenhagen
- Organisation type
- Academia,Academia
- Country
- Denmark
- Published
- 7 July 2017
- Authors
- José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein localization 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
- tokens
- Epochs
- 150
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
- 5.8 × 10¹⁷ FLOP
- How it was established
- Hardware
6691000000000 FLOP / GPU / sec [Titan X reported] * 1 GPU [assumed] * 80 hours * 3600 sec / hour * 0.3 [assumed utilization] = 5.781024e+17 FLOP
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 GeForce GTX TITAN X
- Chips used
- 1
- Wall-clock time
- 80 hours
- Power draw
- 287 W
"The training time for the full ensemble was 80 hours, approximately five hours per model. "
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
- Hosted access (no API)
- Training code
- Unreleased
The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc. Example code is available at https://github.com/JJAlmagro/subcellular_localization. The dataset is available at http://www.cbs.dtu.dk/services/DeepLoc/data.php.
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
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- DeepLoc: prediction of protein subcellular localization using deep learning
- Last updated
- 28 November 2025
What the numbers mean
About this model
DeepLoc was published by Technical University of Denmark,University of Copenhagen, in the country recorded as Denmark, during July 2017. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein localization prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training it took a computation budget of roughly 5.8 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Its inclusion criterion: sOTA improvement.
Answers
DeepLoc — common questions
DeepLoc— who created it?
It was published by Technical University of Denmark,University of Copenhagen, based in Denmark, an organisation categorised as academia,Academia.
DeepLoc— when was it released?
It was published in July 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
DeepLoc— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein localization prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DeepLoc— how much compute was used to train it?
Training consumed around 5.8 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX TITAN X. 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.
DeepLoc— what GPU do I need to run it?
None. This 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.
DeepLoc— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DeepLoc— 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.
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.