Date of Release	Jan 10, 2023
Model Type	Object Detection
Architecture	YOLO, CNN
Framework Used	PyTorch
Annotation Format	YOLOv8 PyTorch TXT
Stars on GitHub	1200+

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5. YOLOv8, launched on January 10, 2023, features:

A new backbone network;
A design that makes it easy to compare model performance with older models in the YOLO family;
A new loss function and;
A new anchor-free detection head.

YOLOv8 Performance: Benchmarked on Roboflow 100

We benchmarked YOLOv8 on Roboflow 100, an object detection benchmark that analyzes the performance of a model in task-specific domains. Roboflow 100 is a method of effectively assessing the extent to which a model can generalize across different problems.

YOLOv8 vs. YOLOv5

We found that YOLOv8 scored a 80.2% mAP score on Roboflow 100, compared to 73.5% mean score on YOLOv5. This shows that YOLOv8 is significantly better at domain-specific tasks than Ultralytics’ YOLOv5 predecessor. We compared YOLOv5s and YOLOv8 in this analysis.

YOLOv8 Perfomance: Benchmarked on COCO

Model	size ^(pixels)	mAP^val 50-95	Speed ^CPU (ms)	Speed ^{T4 GPU (ms)}	params ^(M)	FLOPs ^(B)
YOLOv8n	640	37.3	-	-	3.2	8.7
YOLOv8s	640	44.9	-	-	11.2	28.6
YOLOv8m	640	50.2	-	-	25.9	78.9
YOLOv8l	640	52.9	-	-	43.7	165.2
YOLOv8x	640	53.9	-	-	68.2	257.8

(The table above is sourced from the official YOLOv8 repository).

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

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How to Train YOLOv8 Object Detection on a Custom Dataset

In this article, we walk through how to train a YOLOv8 object detection model using a custom dataset.

Deploy YOLOv8 to production

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

Deploy YOLOv8 Instance Segmentation Models to GCP Compute Engine

Deploy YOLOv8 Object Detection Models to Azure Virtual Machines

Deploy YOLOv8 Object Detection Models to AWS EC2

Deploy YOLOv8 Object Detection Models to GCP Compute Engine

Deploy YOLOv8 Instance Segmentation Models to the Raspberry Pi

Deploy YOLOv8 Classification Models to the Raspberry Pi

Deploy YOLOv8 Object Detection using Docker (CPU)

Deploy YOLOv8 Object Detection Models to the NVIDIA Jetson

Deploy YOLOv8 Object Detection Models to the Raspberry Pi

YOLOv8 Annotation Format

YOLOv8

uses the

YOLOv8 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 YOLOv8

You can automatically label a dataset using

YOLOv8

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

YOLOv8

to train a computer vision model.

Grounding DINO and YOLOv8

SAM-CLIP and YOLOv8

Grounded SAM and YOLOv8