3-ensemble of Self-ensembles on CIFAR-100
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
- Google DeepMind
- Organisation type
- Industry
- Country
- United States of America
- Published
- 8 August 2024
- Authors
- Stanislav Fort, Balaji Lakshminarayanan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Base model
- ResNet-152 (ImageNet)
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
- 60.2M
- Training data
- tokens
- Epochs
- 13
as in resnet-152
"We focused on the CIFAR-* datasets (Krizhevsky, 2009; Krizhevsky et al.) that comprise 50,000 32 × 32 × 3 images. We arbitrarily chose 𝑁 = 4 and the resolutions we used are 32 × 32, 16 × 16, 8 × 8, 4 × 4 (see Figure 3)." Assuming (!) each image (32×32+16×16+8×8+4×4)×3 = 4080 tokens then the entire dataset was 50,000*4080 = 204000000 tokens
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.
- How it was established
- Operation counting,Hardware
- Fine-tuning compute
- 9.6 × 10¹⁷ FLOP
6ND = 6 FLOP / token / parameter * 60200000 parameters* 204000000 tokens * 13 epochs = 9.579024e+17 FLOP "On an A100, the Colab runs for 20 min for the backbone training (6 epochs) but more epochs and especially lower learning rate training later helps. " 312000000000000 FLOP / GPU / sec [bf16 assumed] * 1 GPU * 20 minutes *60 sec / minute * 0.3 [assumed utilization] = 1.1232e+17 FLOP (an underestimation since the training took more epochs)
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 A100
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
- Open source
MIT license https://github.com/stanislavfort/ensemble-everything-everywhere
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness
- Last updated
- 28 November 2025
What the numbers mean
Background
3-ensemble of Self-ensembles on CIFAR-100 was published by Google DeepMind, in United States of America, in August 2024. It comes out of industry.
It works in Vision, and is recorded as doing image classification.
Its starting point was ResNet-152 (ImageNet) — most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
3-ensemble of Self-ensembles on CIFAR-100 — common questions
Who created 3-ensemble of Self-ensembles on CIFAR-100?
3-ensemble of Self-ensembles on CIFAR-100 was published by Google DeepMind, based in United States of America, categorised as industry.
When was 3-ensemble of Self-ensembles on CIFAR-100 released?
3-ensemble of Self-ensembles on CIFAR-100 was published in August 2024. 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 3-ensemble of Self-ensembles on CIFAR-100 used for?
3-ensemble of Self-ensembles on CIFAR-100 works in Vision, and is recorded as handling 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.
What GPU do I need to run 3-ensemble of Self-ensembles on CIFAR-100?
None. 3-ensemble of Self-ensembles on CIFAR-100 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 3-ensemble of Self-ensembles on CIFAR-100 open source?
No. 3-ensemble of Self-ensembles on CIFAR-100 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does 3-ensemble of Self-ensembles on CIFAR-100 have?
3-ensemble of Self-ensembles on CIFAR-100 has 60.2M parameters. as in resnet-152. 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.