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YOLOv8

Overview

YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various object detection tasks in a wide range of applications.

Ultralytics YOLOv8



Watch: Ultralytics YOLOv8 Model Overview

Key Features

  • Advanced Backbone and Neck Architectures: YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and object detection performance.
  • Anchor-free Split Ultralytics Head: YOLOv8 adopts an anchor-free split Ultralytics head, which contributes to better accuracy and a more efficient detection process compared to anchor-based approaches.
  • Optimized Accuracy-Speed Tradeoff: With a focus on maintaining an optimal balance between accuracy and speed, YOLOv8 is suitable for real-time object detection tasks in diverse application areas.
  • Variety of Pre-trained Models: YOLOv8 offers a range of pre-trained models to cater to various tasks and performance requirements, making it easier to find the right model for your specific use case.

Supported Tasks and Modes

The YOLOv8 series offers a diverse range of models, each specialized for specific tasks in computer vision. These models are designed to cater to various requirements, from object detection to more complex tasks like instance segmentation, pose/keypoints detection, oriented object detection, and classification.

Each variant of the YOLOv8 series is optimized for its respective task, ensuring high performance and accuracy. Additionally, these models are compatible with various operational modes including Inference, Validation, Training, and Export, facilitating their use in different stages of deployment and development.

Model Filenames Task Inference Validation Training Export
YOLOv8 yolov8n.pt yolov8s.pt yolov8m.pt yolov8l.pt yolov8x.pt Detection
YOLOv8-seg yolov8n-seg.pt yolov8s-seg.pt yolov8m-seg.pt yolov8l-seg.pt yolov8x-seg.pt Instance Segmentation
YOLOv8-pose yolov8n-pose.pt yolov8s-pose.pt yolov8m-pose.pt yolov8l-pose.pt yolov8x-pose.pt yolov8x-pose-p6.pt Pose/Keypoints
YOLOv8-obb yolov8n-obb.pt yolov8s-obb.pt yolov8m-obb.pt yolov8l-obb.pt yolov8x-obb.pt Oriented Detection
YOLOv8-cls yolov8n-cls.pt yolov8s-cls.pt yolov8m-cls.pt yolov8l-cls.pt yolov8x-cls.pt Classification

This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in computer vision.

Performance Metrics

Performance

See Detection Docs for usage examples with these models trained on COCO, which include 80 pre-trained classes.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
A100 TensorRT
(ms)
params
(M)
FLOPs
(B)
YOLOv8n 640 37.3 80.4 0.99 3.2 8.7
YOLOv8s 640 44.9 128.4 1.20 11.2 28.6
YOLOv8m 640 50.2 234.7 1.83 25.9 78.9
YOLOv8l 640 52.9 375.2 2.39 43.7 165.2
YOLOv8x 640 53.9 479.1 3.53 68.2 257.8

See Detection Docs for usage examples with these models trained on Open Image V7, which include 600 pre-trained classes.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
A100 TensorRT
(ms)
params
(M)
FLOPs
(B)
YOLOv8n 640 18.4 142.4 1.21 3.5 10.5
YOLOv8s 640 27.7 183.1 1.40 11.4 29.7
YOLOv8m 640 33.6 408.5 2.26 26.2 80.6
YOLOv8l 640 34.9 596.9 2.43 44.1 167.4
YOLOv8x 640 36.3 860.6 3.56 68.7 260.6

See Segmentation Docs for usage examples with these models trained on COCO, which include 80 pre-trained classes.

Model size
(pixels)
mAPbox
50-95
mAPmask
50-95
Speed
CPU ONNX
(ms)
Speed
A100 TensorRT
(ms)
params
(M)
FLOPs
(B)
YOLOv8n-seg 640 36.7 30.5 96.1 1.21 3.4 12.6
YOLOv8s-seg 640 44.6 36.8 155.7 1.47 11.8 42.6
YOLOv8m-seg 640 49.9 40.8 317.0 2.18 27.3 110.2
YOLOv8l-seg 640 52.3 42.6 572.4 2.79 46.0 220.5
YOLOv8x-seg 640 53.4 43.4 712.1 4.02 71.8 344.1

See Classification Docs for usage examples with these models trained on ImageNet, which include 1000 pre-trained classes.

Model size
(pixels)
acc
top1
acc
top5
Speed
CPU ONNX
(ms)
Speed
A100 TensorRT
(ms)
params
(M)
FLOPs
(B) at 640
YOLOv8n-cls 224 69.0 88.3 12.9 0.31 2.7 4.3
YOLOv8s-cls 224 73.8 91.7 23.4 0.35 6.4 13.5
YOLOv8m-cls 224 76.8 93.5 85.4 0.62 17.0 42.7
YOLOv8l-cls 224 76.8 93.5 163.0 0.87 37.5 99.7
YOLOv8x-cls 224 79.0 94.6 232.0 1.01 57.4 154.8

See Pose Estimation Docs for usage examples with these models trained on COCO, which include 1 pre-trained class, 'person'.

Model size
(pixels)
mAPpose
50-95
mAPpose
50
Speed
CPU ONNX
(ms)
Speed
A100 TensorRT
(ms)
params
(M)
FLOPs
(B)
YOLOv8n-pose 640 50.4 80.1 131.8 1.18 3.3 9.2
YOLOv8s-pose 640 60.0 86.2 233.2 1.42 11.6 30.2
YOLOv8m-pose 640 65.0 88.8 456.3 2.00 26.4 81.0
YOLOv8l-pose 640 67.6 90.0 784.5 2.59 44.4 168.6
YOLOv8x-pose 640 69.2 90.2 1607.1 3.73 69.4 263.2
YOLOv8x-pose-p6 1280 71.6 91.2 4088.7 10.04 99.1 1066.4

See Oriented Detection Docs for usage examples with these models trained on DOTAv1, which include 15 pre-trained classes.

Model size
(pixels)
mAPtest
50
Speed
CPU ONNX
(ms)
Speed
A100 TensorRT
(ms)
params
(M)
FLOPs
(B)
YOLOv8n-obb 1024 78.0 204.77 3.57 3.1 23.3
YOLOv8s-obb 1024 79.5 424.88 4.07 11.4 76.3
YOLOv8m-obb 1024 80.5 763.48 7.61 26.4 208.6
YOLOv8l-obb 1024 80.7 1278.42 11.83 44.5 433.8
YOLOv8x-obb 1024 81.36 1759.10 13.23 69.5 676.7

Usage Examples

This example provides simple YOLOv8 training and inference examples. For full documentation on these and other modes see the Predict, Train, Val and Export docs pages.

Note the below example is for YOLOv8 Detect models for object detection. For additional supported tasks see the Segment, Classify, OBB docs and Pose docs.

Example

PyTorch pretrained *.pt models as well as configuration *.yaml files can be passed to the YOLO() class to create a model instance in python:

from ultralytics import YOLO

# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Display model information (optional)
model.info()

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")

CLI commands are available to directly run the models:

# Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640

# Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image
yolo predict model=yolov8n.pt source=path/to/bus.jpg

Citations and Acknowledgements

If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:

@software{yolov8_ultralytics,
  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
  title = {Ultralytics YOLOv8},
  version = {8.0.0},
  year = {2023},
  url = {https://github.com/ultralytics/ultralytics},
  orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
  license = {AGPL-3.0}
}

Please note that the DOI is pending and will be added to the citation once it is available. YOLOv8 models are provided under AGPL-3.0 and Enterprise licenses.



Created 2023-11-12, Updated 2024-06-10
Authors: glenn-jocher (15), Burhan-Q (1), Laughing-q (2), AyushExel (1), fcakyon (1)

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