Unsupervised High-level Feature Learner
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 July 2012
- Authors
- Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Approach
- Unsupervised
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
- 1B
- Training data
- 1,200,000,000,000 tokens
"To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet)"
10 million 200x200 images extracted from Youtube videos
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
- 6 × 10¹⁷ FLOP
- How it was established
- Operation counting
Assuming 1 epoch, 10 million images and 1 billion parameters, 6*N*D = 6*10^17 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
- 72 hours
- Compute cost
- $16
"We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. "
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Highly cited,SOTA improvement
- Record confidence
- Likely
- Citations
- 2,909
"we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art"
Sources
Where this record came from and when it was last checked.
- Reference
- Building High-level Features Using Large Scale Unsupervised Learning
- Last updated
- 28 November 2025
What the numbers mean
About this model
Unsupervised High-level Feature Learner was published by Google, in the country recorded as United States of America, during July 2012. The category the publisher falls under is industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Training it took a computation budget of roughly 6 × 10¹⁷ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 1,200,000,000,000 tokens of text.
Its inclusion criterion: highly cited,SOTA improvement.
Answers
Unsupervised High-level Feature Learner — common questions
Unsupervised High-level Feature Learner— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. 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.
Unsupervised High-level Feature Learner— how much compute was used to train it?
Training consumed around 6 × 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.
Unsupervised High-level Feature Learner— 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.
Unsupervised High-level Feature Learner— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Unsupervised High-level Feature Learner— how many parameters does it have?
It has a parameter count of 1B. "To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet)". 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.
Unsupervised High-level Feature Learner— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
Unsupervised High-level Feature Learner— when was it released?
It was published in July 2012. 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.