What is YOLOv5?

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

About the model

Here is an overview of the

YOLOv5

model:

Date of Release Jan 06, 2020
Model Type Object Detection
Architecture CNN, YOLO
Framework Used PyTorch
Annotation Format YOLOv5 PyTorch TXT
Stars on GitHub 33200+

YOLOv5 is Here

YOLOv5 was released by Glenn Jocher on June 9, 2020. It follows the recent releases of YOLOv4 (April 23, 2020) and EfficientDet (March 18, 2020).

YOLO Inference

YOLOv5 Performance

YOLOv5 is smaller and generally easier to use in production. Given it is natively implemented in PyTorch (rather than Darknet), modifying the architecture and exporting to many deploy environments is straightforward.

  • SIZE: YOLOv5s is about 88% smaller than big-YOLOv4 (27 MB vs 244 MB)
YOLOv5 Size
  • SPEED: YOLOv5 performs batch inference at about 140 FPS by default.
  • ACCURACY: YOLOv5 is roughly as accurate as YOLOv4 on small tasks (0.895 mAP vs 0.892 mAP on BCCD). On larger tasks like COCO, YOLOv4 is more performant.

Read more about YOLOv5 performance.

YOLOv5 Performance

Using YOLOv5

We've written both a YOLOv5 tutorial and YOLOv5 Colab notebook for training YOLOv5 on your own custom data.

2022 YOLOv5 releases Classification and Instance Segmentation

YOLOv5 launched supporting bounding boxes for object detection. Now you can use YOLOv5 for classification and instance segmentation as well.

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Model Performance

Model size
(pixels)
mAPbox
50-95
mAPmask
50-95
Train time
300 epochs
A100 (hours)
Speed
ONNX CPU
(ms)
Speed
TRT A100
(ms)
params
(M)
FLOPs
@640 (B)
YOLOv5n-seg 640 27.6 23.4 80:17 62.7 1.2 2.0 7.1
YOLOv5s-seg 640 37.6 31.7 88:16 173.3 1.4 7.6 26.4
YOLOv5m-seg 640 45.0 37.1 108:36 427.0 2.2 22.0 70.8
YOLOv5l-seg 640 49.0 39.9 66:43 (2x) 857.4 2.9 47.9 147.7
YOLOv5x-seg 640 50.7 41.4 62:56 (3x) 1579.2 4.5 88.8 265.7

Explore this model on Roboflow

Deploy YOLOv5 to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

YOLOv5 Annotation Format

YOLOv5

uses the

uses the

YOLOv5 PyTorch TXT

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

Convert data between formats

Label data automatically with YOLOv5

You can automatically label a dataset using

YOLOv5

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

YOLOv5

to train a computer vision model.

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