xn-ultralytics

v1.0.3

This is a public fork of Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. Free for anyone to use, for non-commercial purposes. For more information about how to use this package see README

Latest version published 1 year ago

License: AGPL-3.0

PyPI

GitHub

Package Health Score

61 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Active

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Popularity

Small

GitHub Stars: 33.57K
Forks: 6.45K
Contributors: 210

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package xn-ultralytics, we found that it has been starred 33,567 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Oct 3, 2023
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 1
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

AGPL-3.0

Non-Permissive License

We noticed that this project uses a license which requires less permissive conditions such as disclosing the source code, stating changes or redistributing the source under the same license. It is advised to further consult the license terms before use.

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 1.09K
Open PR: 187
Last Release: 1 year ago
Last Commit: 13 days ago

Further analysis of the maintenance status of xn-ultralytics based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for xn-ultralytics is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 210
Funding: Yes

A good and healthy external contribution signal for xn-ultralytics project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.7

Age: 1 year
Latest Release: 1 year ago
Dependencies: 12 Direct / 32 Total
Versions: 4
Maintainers: 0
Wheels: OS Independent

Readme

This is a personal fork of Ultralytics because I want to be able to make modifications and pip install it.

English | 简体中文

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>=3.7 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
path = 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.

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

See Detection Docs for usage examples with these models.

Model	size ^(pixels)	mAP^val 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

mAP^val 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)	mAP^box 50-95	mAP^mask 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

mAP^val 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)	acc ^top1	acc ^top5	Speed ^{CPU ONNX (ms)}	Speed ^{A100 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

Pose

See Pose Docs for usage examples with these models.

Model	size ^(pixels)	mAP^pose 50-95	mAP^pose 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

mAP^val 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=coco8-pose.yaml 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 🚀 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:

AGPL-3.0 License: See LICENSE file for details.
Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-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, and join our Discord community for questions and discussions!

xn-ultralytics dependencies

matplotlib opencv-python pandas pillow psutil pyyaml requests scipy seaborn torch torchvision tqdm

FAQs

What is xn-ultralytics?

This is a public fork of Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, po... Visit Snyk Advisor to see a full health score report for xn-ultralytics, including popularity, security, maintenance & community analysis.

Is xn-ultralytics popular?

The python package xn-ultralytics receives a total of 52 weekly downloads. As such, xn-ultralytics popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is xn-ultralytics well maintained?

We found indications that xn-ultralytics is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is xn-ultralytics safe to use?

While scanning the latest version of xn-ultralytics, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 12 December-2024, at 07:37 (UTC).

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Readme

Documentation

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Ultralytics HUB

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License

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