YOLOv8: State-of-the-Art Computer Vision Model

What is YOLOv8?

YOLOv8 is a computer vision model architecture developed by Ultralytics, the creators of YOLOv5. You can deploy YOLOv8 models on a wide range of devices, including NVIDIA Jetson, NVIDIA GPUs, and macOS systems with Roboflow Inference, an open source Python package for running vision models.

What is YOLOv8?

YOLOv8 is a computer vision model architecture developed by Ultralytics, the creators of YOLOv5. You can deploy YOLOv8 models on a wide range of devices, including NVIDIA Jetson, NVIDIA GPUs, and macOS systems with Roboflow Inference, an open source Python package for running vision models.

Deploy a YOLOv8 Model

To deploy a YOLOv8 model, first install Inference with pip install inference. Then:

 from inference import get_model
import supervision as sv
from inference.core.utils.image_utils import load_image_bgr
 
image = load_image_bgr("https://media.roboflow.com/inference/vehicles.png")
model = get_model(model_id="yolov8n-640")
results = model.infer(image)[0]
results = sv.Detections.from_inference(results)
annotator = sv.BoxAnnotator(thickness=4)
annotated_image = annotator.annotate(image, results)
annotator = sv.LabelAnnotator(text_scale=2, text_thickness=2)
annotated_image = annotator.annotate(annotated_image, results)
sv.plot_image(annotated_image)

 from inference import InferencePipeline
from inference.core.interfaces.stream.sinks import render_boxes
 
pipeline = InferencePipeline.init(
    model_id="yolov8n-640",
    video_reference=0,
    on_prediction=render_boxes
)
pipeline.start()
pipeline.join()

Find YOLOv8 Datasets

Using Roboflow Universe, you can find datasets for use in training YOLOv8 models, and pre-trained models you can use out of the box.

Selected YOLOv8 Models and Datasets

Plane Detection

Count airplanes photographed using aerial imagery techniques.

Warehouse Item Detection

Detect forklifts, pallets, and small load carriers, among other items you would find in a warehouse environment.

License Plate Detection

Identify license plates in an image.

Search Roboflow Universe

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Train a YOLOv8 Model

Notebooks

YOLOv8 Object Detection

YOLOv8 Keypoint Detection

YOLOv8 Image Classification

YOLOv8 Instance Segmentation

YOLOv8 Oriented Bounding Boxes

You can train a YOLOv8 model using the Ultralytics command line interface.

To train a model, install Ultralytics:

pip install ultralytics

Then, use the following command to train your model:

 yolo task=detect
mode=train
model=yolov8s.pt
data=dataset/data.yaml
epochs=100
imgsz=640

Replace data with the name of your YOLOv8-formatted dataset. Learn more about the YOLOv8 format.

You can then test your model on images in your test dataset with the following command:

 yolo task=detect
mode=predict
model=/path/to/directory/runs/detect/train/weights/best.pt
conf=0.25
source=dataset/test/images

Once you have a model, you can deploy it with Roboflow.

Deploy Your YOLOv8 Model

YOLOv8 Model Sizes

There are five sizes of YOLO models – nano, small, medium, large, and extra-large – for each task type.

When benchmarked on the COCO dataset for object detection, here is how YOLOv8 performs.

Model

Size (px)

mAP^val

YOLOv8n

640

37.3

YOLOv8s

640

44.9

YOLOv8m

640

50.2

YOLOv8l

640

52.9

YOLOv8x

640

53.9

Explore Ultralytics YOLOv8

What is YOLOv8?

What is YOLOv8?

Deploy a YOLOv8 Model

Find YOLOv8 Datasets

Selected YOLOv8 Models and Datasets

Search Roboflow Universe

Train a YOLOv8 Model

Notebooks

YOLOv8 Model Sizes

Frequently Asked Questions

	from inference import get_model
	import supervision as sv
	from inference.core.utils.image_utils import load_image_bgr

	image = load_image_bgr("https://media.roboflow.com/inference/vehicles.png")
	model = get_model(model_id="yolov8n-640")
	results = model.infer(image)[0]
	results = sv.Detections.from_inference(results)
	annotator = sv.BoxAnnotator(thickness=4)
	annotated_image = annotator.annotate(image, results)
	annotator = sv.LabelAnnotator(text_scale=2, text_thickness=2)
	annotated_image = annotator.annotate(annotated_image, results)
	sv.plot_image(annotated_image)

	from inference import InferencePipeline
	from inference.core.interfaces.stream.sinks import render_boxes

	pipeline = InferencePipeline.init(
	model_id="yolov8n-640",
	video_reference=0,
	on_prediction=render_boxes
	)
	pipeline.start()
	pipeline.join()

	yolo task=detect
	mode=train
	model=yolov8s.pt
	data=dataset/data.yaml
	epochs=100
	imgsz=640

	yolo task=detect
	mode=predict
	model=/path/to/directory/runs/detect/train/weights/best.pt
	conf=0.25
	source=dataset/test/images