GitLocker: The Coding Marketplace

Description:

imagedetect 8.0.57

English | 简体中文

Ultralytics YOLOv8, developed by Ultralytics,
is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces
new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and
easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image
classification tasks.
To request an Enterprise License please complete the form at Ultralytics Licensing.

Documentation
See below for a quickstart installation and usage example, and see the YOLOv8 Docs for
full documentation on training, validation, prediction and deployment.

Install
Pip install the ultralytics package including
all requirements.txt in a
Python>=3.7 environment with
PyTorch>=1.7.
pip install ultralytics

Usage
CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:
yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"

yolo can be used for a variety of tasks and modes and accepts additional arguments, i.e. imgsz=640. See the YOLOv8
CLI Docs for examples.
Python
YOLOv8 may also be used directly in a Python environment, and accepts the
same arguments as in the CLI example above:
from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)

# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = model.export(format="onnx") # export the model to ONNX format

Models download automatically from the latest
Ultralytics release. See
YOLOv8 Python Docs for more examples.
Model Architectures
⭐ NEW YOLOv5u anchor free models are now available.
All supported model architectures can be found in the Models section.
Known Issues / TODOs
We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
will submit to arxiv.org once complete.

TensorFlow exports
DDP resume
arxiv.org paper

Models
All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
while Classification models are pretrained on the ImageNet dataset.
Models download automatically from the latest
Ultralytics release on first use.
Detection
See Detection Docs for usage examples with these models.

Model
size(pixels)
mAPval50-95
SpeedCPU ONNX(ms)
SpeedA100 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

mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce by yolo val detect data=coco.yaml device=0
Speed averaged over COCO val images using an Amazon EC2 P4d
instance.
Reproduce by yolo val detect data=coco128.yaml batch=1 device=0/cpu

Segmentation
See Segmentation Docs for usage examples with these models.

Model
size(pixels)
mAPbox50-95
mAPmask50-95
SpeedCPU ONNX(ms)
SpeedA100 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

mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce by yolo val segment data=coco.yaml device=0
Speed averaged over COCO val images using an Amazon EC2 P4d
instance.
Reproduce by yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu

Classification
See Classification Docs for usage examples with these models.

Model
size(pixels)
acctop1
acctop5
SpeedCPU ONNX(ms)
SpeedA100 TensorRT(ms)
params(M)
FLOPs(B) at 640

YOLOv8n-cls
224
66.6
87.0
12.9
0.31
2.7
4.3

YOLOv8s-cls
224
72.3
91.1
23.4
0.35
6.4
13.5

YOLOv8m-cls
224
76.4
93.2
85.4
0.62
17.0
42.7

YOLOv8l-cls
224
78.0
94.1
163.0
0.87
37.5
99.7

YOLOv8x-cls
224
78.4
94.3
232.0
1.01
57.4
154.8

acc values are model accuracies on the ImageNet dataset validation set.
Reproduce by yolo val classify data=path/to/ImageNet device=0
Speed averaged over ImageNet val images using an Amazon EC2 P4d
instance.
Reproduce by yolo val classify data=path/to/ImageNet batch=1 device=0/cpu

Integrations

Roboflow
ClearML ⭐ NEW
Comet ⭐ NEW
Neural Magic ⭐ NEW

Label and export your custom datasets directly to YOLOv8 for training with Roboflow
Automatically track, visualize and even remotely train YOLOv8 using ClearML (open-source!)
Free forever, Comet lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions
Run YOLOv8 inference up to 6x faster with Neural Magic DeepSparse

Ultralytics HUB
Experience seamless AI with Ultralytics HUB ⭐, the all-in-one solution for data
visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images
into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and
user-friendly Ultralytics App. Start your journey for Free now!

Contribute
We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see
our Contributing Guide to get started, and fill out
our Survey to send us feedback
on your experience. Thank you 🙏 to all our contributors!

License
YOLOv8 is available under two different licenses:

GPL-3.0 License: See LICENSE file for details.
Enterprise License: Provides greater flexibility for commercial product development without the open-source
requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and
applications. Request an Enterprise License at Ultralytics Licensing.

Contact
For YOLOv8 bug reports and feature requests please
visit GitHub Issues or
the Ultralytics Community Forum.