YOLOv3
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
- University of Washington
- Organisation type
- Academia
- Country
- United States of America
- Published
- 8 April 2018
- Authors
- Joseph Redmon, Ali Farhadi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- 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
- 56.9M
- Training data
- 5,430,000 tokens
Feature extractor (ignoring biases) 32*3*3*3 + 64*3*3*32 + 32*1*1*64 + 64*3*3*32 + 128*3*3*64 + 2*(64*1*1*128 + 128*3*3*64) + 256*3*3*128 + 8*(128*1*1*256 + 256*3*3*128) + 512*3*3*256 + 8*(256*1*1*512 + 512*3*3*256) + 1024*3*3*512 + 4*(512*1*1*1024 + 1024*3*3*512) + 4*4*1024*1000 source: table 1 This is assuming the average pooling step changes the output size from 8x8 to 4x4. The weights file is 237MB. If the weights are saved as float32, 4 bytes per weight, then there are approximately 2…
Source: https://image-net.org/download.php
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
- 1.3 × 10¹⁹ FLOP
- How it was established
- Operation counting
We use the formula training_compute = ops_per_forward_pass * 3.5 * n_epochs * n_examples Assuming 160 epochs of training as in https://arxiv.org/pdf/1612.08242.pdf Table 2: 18700000000 operations 18700000000 ops * 3.5 *160 epochs * 1281167 images
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 M40,NVIDIA GeForce GTX TITAN X
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
- Unreleased
code and weights, unclear license: https://pjreddie.com/darknet/yolo/
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
- Likely
- Citations
- 25,147
Sources
Where this record came from and when it was last checked.
- Reference
- YOLOv3: An Incremental Improvement
- Last updated
- 25 May 2026
What the numbers mean
What this model is
YOLOv3 was published by University of Washington, in United States of America, in April 2018. The organisation is categorised as academia.
It works in Vision, 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.
Training and provenance
Training it took roughly 1.3 × 10¹⁹ FLOP of computation, on NVIDIA M40,NVIDIA GeForce GTX TITAN X — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 5,430,000 tokens of text.
Its inclusion criterion is highly cited.
Answers
YOLOv3 — common questions
Is YOLOv3 open source?
No. YOLOv3 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does YOLOv3 have?
YOLOv3 has 56.9M parameters. Feature extractor (ignoring biases) 32*3*3*3 + 64*3*3*32 + 32*1*1*64 + 64*3*3*32 + 128*3*3*64 + 2*(64*1*1*128 + 128*3*3*64) + 256*3*3*128 + 8*(128*1*1*256 + 256*3*3*128) + 512*3*3*256 + 8*(256*1*1*512 + 512*3*3*256) + 1024*3*3*512 + 4*(512*1*1*1024 + 1024*3*3*512) + 4*4*1024*1000 source: table 1 This is assuming the average pooling step changes the output size from 8x8 to 4x4. The weights file is 237MB. If the weights are saved as float32, 4 bytes per weight, then there are approximately 237M/4=59M parameters, consistent with the calculation above. 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 YOLOv3?
YOLOv3 was published by University of Washington, based in United States of America, categorised as academia.
When was YOLOv3 released?
YOLOv3 was published in April 2018. 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 YOLOv3 used for?
YOLOv3 works in Vision, and is recorded as handling object detection. 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.
How much compute was used to train YOLOv3?
Around 1.3 × 10¹⁹ FLOP, on NVIDIA M40,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.
What GPU do I need to run YOLOv3?
None. YOLOv3 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.
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.