ultralytics-nogui 8.2.52

Description:

ultralyticsnogui 8.2.52

ultralytics-nogui
This is a fork of the Ultralytics library. The only difference is that this repo uses opencv-python-headless instead of opencv-python.
The original README





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Ultralytics YOLOv8 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 and tracking, instance segmentation, image classification and pose estimation tasks.
We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!
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 in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

For alternative installation methods including Conda, Docker, and Git, please refer to the Quickstart Guide.



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="coco8.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
path = model.export(format="onnx") # export the model to ONNX format

See YOLOv8 Python Docs for more examples.

Notebooks
Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a YouTube tutorial, making it easy to learn and implement advanced YOLOv8 features.



Docs
Notebook
YouTube




YOLOv8 Train, Val, Predict and Export Modes




Ultralytics HUB QuickStart




YOLOv8 Multi-Object Tracking in Videos




YOLOv8 Object Counting in Videos




YOLOv8 Heatmaps in Videos




Ultralytics Datasets Explorer with SQL and OpenAI Integration 🚀 New





Models
YOLOv8 Detect, Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLOv8 Classify models pretrained on the ImageNet dataset. Track mode is available for all Detect, Segment and Pose models.

All Models download automatically from the latest Ultralytics release on first use.
Detection (COCO)
See Detection Docs for usage examples with these models trained on COCO, which include 80 pre-trained classes.



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=coco.yaml batch=1 device=0|cpu


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



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




mAPval values are for single-model single-scale on Open Image V7 dataset. Reproduce by yolo val detect data=open-images-v7.yaml device=0
Speed averaged over Open Image V7 val images using an Amazon EC2 P4d instance. Reproduce by yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu


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



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-seg.yaml device=0
Speed averaged over COCO val images using an Amazon EC2 P4d instance. Reproduce by yolo val segment data=coco-seg.yaml batch=1 device=0|cpu


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



Model
size(pixels)
mAPpose50-95
mAPpose50
SpeedCPU ONNX(ms)
SpeedA100 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




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


OBB (DOTAv1)
See OBB Docs for usage examples with these models trained on DOTAv1, which include 15 pre-trained classes.



Model
size(pixels)
mAPtest50
SpeedCPU ONNX(ms)
SpeedA100 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




mAPtest values are for single-model multiscale on DOTAv1 dataset. Reproduce by yolo val obb data=DOTAv1.yaml device=0 split=test and submit merged results to DOTA evaluation.
Speed averaged over DOTAv1 val images using an Amazon EC2 P4d instance. Reproduce by yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu


Classification (ImageNet)
See Classification Docs for usage examples with these models trained on ImageNet, which include 1000 pretrained classes.



Model
size(pixels)
acctop1
acctop5
SpeedCPU ONNX(ms)
SpeedA100 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
78.3
94.2
163.0
0.87
37.5
99.7


YOLOv8x-cls
224
79.0
94.6
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
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with Roboflow, ClearML, Comet, Neural Magic and OpenVINO, can optimize your AI workflow.





















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 🚀 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
Ultralytics offers two licensing options to accommodate diverse use cases:

AGPL-3.0 License: This OSI-approved open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the LICENSE file for more details.
Enterprise License: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through Ultralytics Licensing.

Contact
For Ultralytics bug reports and feature requests please visit GitHub Issues, and join our Discord community for questions and discussions!