ResNet-152 (ImageNet)
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
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 10 December 2015
- Authors
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Numerical format
- FP32
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
- 1,280,000 tokens
- Epochs
- 120
- Batch size
- 256
Taken from https://arxiv.org/abs/1605.07146
"We evaluate our method on the ImageNet 2012 classification dataset [36] that consists of 1000 classes. The models are trained on the 1.28 million training images"
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 × 10¹⁹ FLOP
- How it was established
- Operation counting,Third-party estimation
11.3 *10^9 mult-adds per forward pass (Table 1) 2 FLOPS/ mult-add 3 for forward & backward pass 1.2 * 10^6 examples in dataset 128 epochs -> 1.041408 × 10^19 FLOP Authors of "AI and Memory Wall" (https://github.com/amirgholami/ai_and_memory_wall) estimated model's training compute as 11,000 PFLOP = 1.1*10^19 FLOP
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
- Highly cited
- Record confidence
- Confident
- Citations
- 228,517
Sources
Where this record came from and when it was last checked.
- Reference
- Deep Residual Learning for Image Recognition
- Last updated
- 25 May 2026
What the numbers mean
Background
ResNet-152 (ImageNet) was published by Microsoft, in United States of America, in December 2015. It comes out of industry.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 1 × 10¹⁹ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 1,280,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
ResNet-152 (ImageNet) — common questions
When was ResNet-152 (ImageNet) released?
ResNet-152 (ImageNet) was published in December 2015. 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 ResNet-152 (ImageNet) used for?
ResNet-152 (ImageNet) works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train ResNet-152 (ImageNet)?
Around 1 × 10¹⁹ 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 ResNet-152 (ImageNet)?
None. ResNet-152 (ImageNet) 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 ResNet-152 (ImageNet) open source?
The licensing for ResNet-152 (ImageNet) 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 ResNet-152 (ImageNet) have?
ResNet-152 (ImageNet) has 60.2M parameters. Taken from https://arxiv.org/abs/1605.07146. 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.
Who created ResNet-152 (ImageNet)?
ResNet-152 (ImageNet) was published by Microsoft, based in United States of America, categorised as industry.
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.