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).

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)

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.

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.

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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

Learn about YOLOv8

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

Learn about YOLOv8

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

Learn about YOLOv8

Model Performance

Model	size ^(pixels)	mAP^box 50-95	mAP^mask 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

YOLOv5 for Oriented Object Detection

Oriented bounding boxes are bounding boxes rotated to better fit the objects represented on an angle. Follow this guide to learn more.

Responding to the Controversy about YOLOv5

Retail Store Item Detection using YOLOv5

School Bus Object Detection Using YOLOv5

Learn how to train YOLOv5 to detect a school bus using Roboflow and CometML

What is YOLOv5? A Guide for Beginners.

YOLOv5 derives most of its performance improvement from PyTorch training procedures, while the model architecture remains close to YOLOv4.

How to Train YOLOv5 On a Custom Dataset

In this post, we will walk through how you can train YOLOv5 to recognize your custom objects for your custom use case.

Deploy YOLOv5 to production

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

Deploy YOLOv5 Object Detection Models to NVIDIA Jetson

Deploy YOLOv5 Object Detection Models to Raspberry Pi

Deploy YOLOv5 Classification Models to Raspberry Pi

YOLOv5 Annotation Format

YOLOv5

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.
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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.

OWL-ViT and YOLOv5

Grounding DINO and YOLOv5

SAM-CLIP and YOLOv5

Grounded SAM and YOLOv5