Mitosis
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
- IDSIA
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 22 September 2013
- Authors
- Dan C. Cireşan, Alessandro Giusti, Luca M. Gambardella, Jürgen Schmidhuber
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Medicine
- Task
- Object detection
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
- 37.2K
- Training data
- 1,000,000 tokens
Sum numbers of weights in Table 1.b
The dataset is built in two stages. First a classifier is trained on small sample, and used to curate a more representative larger dataset. The final dataset has 1M instances "We build the actual training set, composed by 1 million instances, which includes all mitosis pixels (6.6% of the training instances). The remaining 95.4% is sampled from non-mitosis pixels by assigning to each pixel p a weight D(p)."
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
- 1.4 × 10¹⁷ FLOP
- How it was established
- Hardware
"Training each network requires one day of computation with an optimized GPU implementation" Assuming 1.58E+12 FLOP/second on FP32 (from the table in the Estimating compute post), we get 3600*24*1.58E+12 = 1.37E+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.
- Wall-clock time
- 24 hours
"Training each network requires one day of computation with an optimized GPU implementation"
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
- 1,553
Sources
Where this record came from and when it was last checked.
- Reference
- Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
- Last updated
- 1 January 2026
What the numbers mean
What this model is
Mitosis was published by IDSIA, in Switzerland, in September 2013. The organisation is categorised as academia.
It works in Vision, Medicine, and is recorded as doing object detection.
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 roughly 1.4 × 10¹⁷ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 1,000,000 tokens of text.
Answers
Mitosis — common questions
Who created Mitosis?
Mitosis was published by IDSIA, based in Switzerland, categorised as academia.
When was Mitosis released?
Mitosis was published in September 2013. 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 Mitosis used for?
Mitosis works in Vision, Medicine, and is recorded as handling object detection. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train Mitosis?
Around 1.4 × 10¹⁷ 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.
What GPU do I need to run Mitosis?
None. Mitosis 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 Mitosis open source?
The licensing for Mitosis was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Mitosis have?
Mitosis has 37.2K parameters. Sum numbers of weights in Table 1.b. 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.
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.