ultralytics

Ultralytics YOLO 🚀

https://github.com/ultralytics/ultralytics

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Keywords

cli computer-vision deep-learning hub image-classification instance-segmentation machine-learning object-detection pose-estimation python pytorch rotated-object-detection segment-anything tracking ultralytics yolo yolo-world yolo11 yolov10 yolov8

Keywords from Contributors

fine-tuning mesh text-to-speech voice-conversion agents hydrology mot cryptocurrencies pipeline-testing datacleaner

Last synced: 4 months ago · JSON representation ·

Repository

Ultralytics YOLO 🚀

Basic Info

Host: GitHub
Owner: ultralytics
License: agpl-3.0
Language: Python
Default Branch: main
Homepage: https://docs.ultralytics.com
Size: 33.4 MB

Statistics

Stars: 44,929
Watchers: 217
Forks: 8,770
Open Issues: 363
Releases: 0

Topics

Created over 3 years ago · Last pushed 4 months ago

Metadata Files

Readme Contributing License Citation

README.md

[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)

Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, tracking, instance segmentation, image classification, and pose estimation tasks.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

📄 Documentation

See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.

Install

Install the `ultralytics` package, including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Ultralytics Downloads](https://static.pepy.tech/badge/ultralytics)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/) ```bash pip install ultralytics ``` For alternative installation methods, including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and building from source via Git, please consult the [Quickstart Guide](https://docs.ultralytics.com/quickstart/). [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics) [![Ultralytics Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)

Usage

### CLI You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command: ```bash # Predict using a pretrained YOLO model (e.g., YOLO11n) on an image yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg' ``` The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for more examples. ### Python Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as the CLI: ```python from ultralytics import YOLO # Load a pretrained YOLO11n model model = YOLO("yolo11n.pt") # Train the model on the COCO8 dataset for 100 epochs train_results = model.train( data="coco8.yaml", # Path to dataset configuration file epochs=100, # Number of training epochs imgsz=640, # Image size for training device="cpu", # Device to run on (e.g., 'cpu', 0, [0,1,2,3]) ) # Evaluate the model's performance on the validation set metrics = model.val() # Perform object detection on an image results = model("path/to/image.jpg") # Predict on an image results[0].show() # Display results # Export the model to ONNX format for deployment path = model.export(format="onnx") # Returns the path to the exported model ``` Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python/).

✨ Models

Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO11. The tables below showcase YOLO11 models pretrained on the COCO dataset for Detection, Segmentation, and Pose Estimation. Additionally, Classification models pretrained on the ImageNet dataset are available. Tracking mode is compatible with all Detection, Segmentation, and Pose models. All Models are automatically downloaded from the latest Ultralytics release upon first use.

Detection (COCO)

Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples. These models are trained on the [COCO dataset](https://cocodataset.org/), featuring 80 object classes. | Model | size
^{(pixels) | mAP^{val
50-95 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |

- **mAP^val** values refer to single-model single-scale performance on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details.
Reproduce with `yolo val detect data=coco.yaml device=0`
- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`}}}}}}

Segmentation (COCO)

Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples. These models are trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), including 80 classes. | Model | size
^{(pixels) | mAP^{box
50-95 | mAP^{mask
50-95 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4 |
| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5 |
| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3 |
| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2 |
| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0 |

- **mAP^val** values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details.
Reproduce with `yolo val segment data=coco.yaml device=0`
- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`}}}}}}}

Classification (ImageNet)

Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples. These models are trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), covering 1000 classes. | Model | size
^{(pixels) | acc
^{top1 | acc
^{top5 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) at 224 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 0.5 |
| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 1.6 |
| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 5.0 |
| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 12.9 | 6.2 |
| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 28.4 | 13.7 |

- **acc** values represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set.
Reproduce with `yolo val classify data=path/to/ImageNet device=0`
- **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`}}}}}}}

Pose (COCO)

See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples. These models are trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), focusing on the 'person' class. | Model | size
^{(pixels) | mAP^{pose
50-95 | mAP^{pose
50 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.6 |
| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.2 |
| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.7 |
| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.2 | 90.7 |
| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 203.3 |

- **mAP^val** values are for single-model single-scale on the [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details.
Reproduce with `yolo val pose data=coco-pose.yaml device=0`
- **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`}}}}}}}

Oriented Bounding Boxes (DOTAv1)

Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples. These models are trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), including 15 classes. | Model | size
^{(pixels) | mAP^{test
50 | Speed
^{CPU ONNX
(ms) | Speed
^{T4 TensorRT10
(ms) | params
^{(M) | FLOPs
^{(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 17.2 |
| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.5 |
| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 183.5 |
| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.2 | 232.0 |
| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 520.2 |

- **mAP^test** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html).
Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https://captain-whu.github.io/DOTA/evaluation.html).
- **Speed** metrics are averaged over [DOTAv1 val images](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10) using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce by `yolo val obb data=DOTAv1.yaml 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 partners like Weights & Biases, Comet ML, Roboflow, and Intel OpenVINO, can optimize your AI workflow. Explore more at Ultralytics Integrations.

| Ultralytics HUB 🌟 | Weights & Biases | Comet | Neural Magic | | :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | | Streamline YOLO workflows: Label, train, and deploy effortlessly with Ultralytics HUB. Try now! | Track experiments, hyperparameters, and results with Weights & Biases. | Free forever, Comet ML lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with Neural Magic DeepSparse. |

🌟 Ultralytics HUB

Experience seamless AI with Ultralytics HUB, the all-in-one platform for data visualization, training YOLO models, and deployment—no coding required. Transform images into actionable insights and bring your AI visions to life effortlessly using our cutting-edge platform and user-friendly Ultralytics App. Start your journey for Free today!

🤝 Contribute

We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our Contributing Guide to get started. We also welcome your feedback—share your experience by completing our Survey. A huge Thank You 🙏 to everyone who contributes!

We look forward to your contributions to help make the Ultralytics ecosystem even better!

📜 License

Ultralytics offers two licensing options to suit different needs:

AGPL-3.0 License: This OSI-approved open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the LICENSE file for full details.
Ultralytics Enterprise License: Designed for commercial use, this license allows for the seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please contact us via Ultralytics Licensing.

📞 Contact

For bug reports and feature requests related to Ultralytics software, please visit GitHub Issues. For questions, discussions, and community support, join our active communities on Discord, Reddit, and the Ultralytics Community Forums. We're here to help with all things Ultralytics!

Owner

Name: Ultralytics
Login: ultralytics
Kind: organization
Email: hello@ultralytics.com
Location: United States of America

Website: https://ultralytics.com
Twitter: ultralytics
Repositories: 14
Profile: https://github.com/ultralytics

Simpler. Smarter. Further.

Citation (CITATION.cff)

# This CITATION.cff file was generated with https://bit.ly/cffinit

cff-version: 1.2.0
title: Ultralytics YOLO
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Glenn
    family-names: Jocher
    affiliation: Ultralytics
    orcid: "https://orcid.org/0000-0001-5950-6979"
  - family-names: Qiu
    given-names: Jing
    affiliation: Ultralytics
    orcid: "https://orcid.org/0000-0003-3783-7069"
  - given-names: Ayush
    family-names: Chaurasia
    affiliation: Ultralytics
    orcid: "https://orcid.org/0000-0002-7603-6750"
repository-code: "https://github.com/ultralytics/ultralytics"
url: "https://ultralytics.com"
license: AGPL-3.0
version: 8.0.0
date-released: "2023-01-10"

Committers

Last synced: 7 months ago

All Time

Total Commits: 2,679
Total Committers: 269
Avg Commits per committer: 9.959
Development Distribution Score (DDS): 0.557

Past Year

Commits: 1,477
Committers: 151
Avg Commits per committer: 9.781
Development Distribution Score (DDS): 0.673

Top Committers

Name	Email	Commits
Glenn Jocher	g**r@u**m	1,188
Muhammad Rizwan Munawar	m**3@g**m	316
Laughing	6****q	211
Mohammed Yasin	3****G	188
Lakshantha Dissanayake	l**d@y**m	96
Ayush Chaurasia	a**a@g**m	83
Burhan	6****Q	48
Francesco Mattioli	F**l@g**m	46
Abirami Vina	a**a@g**m	35
Jan Knobloch	1****e	28
Ultralytics Assistant	1****t	23
dependabot[bot]	4****]	23
Sergiu Waxmann	4****n	18
Kayzwer	6****r	13
Paula Derrenger	1****r	13
Ivor Zhu	1****1	5
Iaroslav Omelianenko	y**l@y**m	5
Kalen Michael	k**e@g**m	5
Adrian Boguszewski	a**i@i**m	5
Skillnoob	7****b	5
fatih c. akyon	3****n	5
Ivan Shcheklein	s**n@g**m	4
Jamjamjon	5****n	4
MatthewNoyce	1****e	4
inisis	4****s	4
Colin Wong	c**h@g**m	4
Onuralp SEZER	t**r@g**m	4
Awsome	1**7@q**m	4
triple Mu	g**u@1**m	4
Quet Almahdi Morris	m**l@g**m	3
and 239 more...

Committer Domains (Top 20 + Academic)

qq.com: 6 intel.com: 5 ultralytics.com: 2 163.com: 2 126.com: 2 henrynavarro.org: 1 romanch.uk: 1 snyk.io: 1 thetribe.team: 1 posteo.de: 1 skiff.com: 1 stu.xidian.edu.cn: 1 frank-ruis.nl: 1 duck.com: 1 iitgn.ac.in: 1 yandex.ru: 1 shankang.com: 1 dismail.de: 1 pereslavl.ru: 1 pyke.io: 1 ntu.edu.tw: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 9,719
Total pull requests: 4,893
Average time to close issues: about 1 month
Average time to close pull requests: 25 days
Total issue authors: 5,855
Total pull request authors: 760
Average comments per issue: 5.0
Average comments per pull request: 3.82
Merged pull requests: 2,687
Bot issues: 4
Bot pull requests: 72

Past Year

Issues: 2,971
Pull requests: 2,641
Average time to close issues: about 1 month
Average time to close pull requests: 5 days
Issue authors: 2,016
Pull request authors: 321
Average comments per issue: 3.59
Average comments per pull request: 3.63
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Bot issues: 1
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question (6,900) Stale (4,975) bug (1,831) detect (837) enhancement (636) non-reproducible (379) fixed (369) exports (346) segment (255) invalid (211) OBB (132) pose (126) dependencies (110) classify (92) embedded (78) track (71) help wanted (68) TODO (53) devops (52) documentation (49) external (42) research (38) solutions (31) duplicate (20) HUB (14) Alert (12) wontfix (5) python (4) enterprise (4) Notebook (4)

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enhancement (1,762) documentation (1,027) devops (405) dependencies (370) exports (359) bug (324) detect (321) TODO (251) Stale (227) solutions (148) segment (128) embedded (124) python (108) question (97) fixed (87) track (82) OBB (68) classify (68) HUB (67) pose (61) invalid (46) Notebook (15) non-reproducible (11) research (10) Explorer (8) popular (7) external (7) duplicate (6) App (5) wontfix (3)

Packages

Total packages: 28
Total downloads:
- pypi 6,071,572 last-month
Total docker downloads: 43,981

Total dependent packages: 177
(may contain duplicates)
Total dependent repositories: 1,178
(may contain duplicates)
Total versions: 1,326
Total maintainers: 19

pypi.org: ultralytics

Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://ultralytics.com
Documentation: https://docs.ultralytics.com
License: AGPL-3.0
Latest release: 8.3.61
published 12 months ago

Versions: 583
Dependent Packages: 177
Dependent Repositories: 1,176
Downloads: 6,067,369 Last month
Docker Downloads: 43,981

Rankings

Stargazers count: 0.1%

Downloads: 0.2%

Forks count: 0.2%

Dependent repos count: 0.3%

Average: 0.5%

Dependent packages count: 0.7%

Docker downloads count: 1.5%

Maintainers (1)

glenn-jocher

Last synced: 12 months ago

pypi.org: ultralytics-dev

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://ultralytics-dev.readthedocs.io/
License: AGPL-3.0
Latest release: 0.0.55
published over 2 years ago

Versions: 47
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 186 Last month

Rankings

Stargazers count: 0.1%

Forks count: 0.2%

Average: 9.0%

Dependent packages count: 10.1%

Downloads: 12.9%

Dependent repos count: 21.5%

Maintainers (1)

glenn-jocher

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: yolotest

Ultralytics YOLOv8

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://yolotest.readthedocs.io/
License: GPL-3.0
Latest release: 8.0.61
published over 2 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 13 Last month

Rankings

Stargazers count: 0.4%

Forks count: 1.3%

Dependent packages count: 7.0%

Average: 9.8%

Dependent repos count: 30.5%

Maintainers (1)

xingyueiwj

Funding

https://ultralytics.com

Last synced: 4 months ago

proxy.golang.org: github.com/ultralytics/ultralytics

Documentation: https://pkg.go.dev/github.com/ultralytics/ultralytics#section-documentation
License: agpl-3.0
Latest release: v8.3.189+incompatible
published 4 months ago

Versions: 554
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 9.3%

Average: 9.9%

Dependent repos count: 10.5%

Last synced: 4 months ago

pypi.org: syml-ultralytics

Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://ultralytics.com
Documentation: https://docs.ultralytics.com
License: AGPL-3.0
Latest release: 8.3.38
published about 1 year ago

Versions: 21
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 81 Last month

Rankings

Stargazers count: 0.1%

Forks count: 0.2%

Average: 10.0%

Dependent packages count: 10.1%

Downloads: 17.8%

Dependent repos count: 21.5%

Maintainers (2)

its-jd synentec

Last synced: 4 months ago

pypi.org: imagedetect

Ultralytics YOLOv8

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://imagedetect.readthedocs.io/
License: GPL-3.0
Latest release: 8.0.57
published almost 3 years ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 25 Last month

Rankings

Stargazers count: 0.4%

Forks count: 1.3%

Dependent packages count: 6.6%

Average: 10.2%

Downloads: 12.3%

Dependent repos count: 30.6%

Maintainers (1)

xingyueiwj

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: roar-yolo

Ultralytics YOLOv8

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://roar-yolo.readthedocs.io/
License: GPL-3.0
Latest release: 9.0.0
published almost 3 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 14 Last month

Rankings

Stargazers count: 0.4%

Forks count: 1.3%

Dependent packages count: 7.0%

Average: 10.4%

Downloads: 12.8%

Dependent repos count: 30.5%

Maintainers (1)

yeyufisher

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: ultralytics-dist-yolo

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://ultralytics-dist-yolo.readthedocs.io/
License: AGPL-3.0
Latest release: 99.99
published over 2 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 44 Last month

Rankings

Stargazers count: 0.2%

Forks count: 0.4%

Dependent packages count: 7.2%

Average: 12.3%

Dependent repos count: 41.3%

Maintainers (1)

FRERE

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: ultralytics-custom

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://ultralytics-custom.readthedocs.io/
License: AGPL-3.0
Latest release: 8.0.112
published over 2 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 9 Last month

Rankings

Stargazers count: 0.3%

Forks count: 1.0%

Dependent packages count: 7.3%

Average: 12.5%

Dependent repos count: 41.3%

Maintainers (1)

MaruyamaAya

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: ppmmvehicle

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://ppmmvehicle.readthedocs.io/
License: AGPL-3.0
Latest release: 1.0.1
published over 2 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 15 Last month

Rankings

Stargazers count: 0.2%

Forks count: 0.3%

Dependent packages count: 7.2%

Average: 13.2%

Downloads: 16.9%

Dependent repos count: 41.3%

Maintainers (1)

Omi123

Funding

https://ultralytics.com

Last synced: about 1 year ago

pypi.org: rknn-yolov8

自用rknn yolov8 版本 8.2.82 只修复了 weights_only=False 问题

Documentation: https://rknn-yolov8.readthedocs.io/
License: AGPL-3.0
Latest release: 8.2.82
published 5 months ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Forks count: 0.1%

Stargazers count: 0.1%

Dependent packages count: 8.7%

Average: 14.5%

Dependent repos count: 49.0%

Maintainers (1)

zbkit

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: zmkj-rknn-yolov8

自用rknn yolov8 版本只修复了 weights_only=False 问题来源https://github.com/airockchip/ultralytics_yolov8 8.0.151 v1.6分支

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://zmkj-rknn-yolov8.readthedocs.io/
License: AGPL-3.0
Latest release: 8.2.82
published 5 months ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 647 Last month

Rankings

Forks count: 0.1%

Stargazers count: 0.1%

Dependent packages count: 8.7%

Average: 14.5%

Dependent repos count: 49.0%

Maintainers (1)

zbkit

Funding

https://ultralytics.com

Last synced: 4 months ago

spack.io: py-ultralytics

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.

Homepage: https://github.com/ultralytics/ultralytics
License: []
Latest release: 8.0.50
published almost 3 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 0.0%

Stargazers count: 2.2%

Forks count: 3.2%

Average: 15.7%

Dependent packages count: 57.3%

Maintainers (1)

adamjstewart

Last synced: 4 months ago

pypi.org: ultralytics-copy

Documentation: https://ultralytics-copy.readthedocs.io/
License: agpl-3.0
Latest release: 0.0.1.dev0
published over 2 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 33 Last month

Rankings

Stargazers count: 0.2%

Forks count: 0.3%

Dependent packages count: 7.5%

Average: 16.3%

Dependent repos count: 56.9%

Maintainers (1)

qslia

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: dgenerate-ultralytics-headless

Automatically built Ultralytics package with python-opencv-headless dependency instead of python-opencv

Homepage: https://ultralytics.com
Documentation: https://docs.ultralytics.com
License: AGPL-3.0
Latest release: 8.3.192
published 4 months ago

Versions: 55
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 2,494 Last month

Rankings

Dependent packages count: 9.1%

Average: 30.3%

Dependent repos count: 51.5%

Maintainers (1)

Teriks

Last synced: 4 months ago

pypi.org: ultralytics-headless

Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://ultralytics.com
Documentation: https://docs.ultralytics.com
License: AGPL-3.0
Latest release: 8.3.33
published about 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 10.0%

Average: 33.2%

Dependent repos count: 56.4%

Maintainers (1)

oliviermirat

Last synced: about 1 year ago

pypi.org: ultralyticsheadless

Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://ultralytics.com
Documentation: https://docs.ultralytics.com
License: AGPL-3.0
Latest release: 8.3.33
published about 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 68 Last month

Rankings

Dependent packages count: 10.0%

Average: 33.2%

Dependent repos count: 56.4%

Maintainers (1)

oliviermirat

Last synced: 4 months ago

pypi.org: ultralytics-v11

Ultralytics YOLO for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://ultralytics.com
Documentation: https://docs.ultralytics.com
License: AGPL-3.0
Latest release: 8.3.9
published about 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 116 Last month

Rankings

Dependent packages count: 10.2%

Average: 33.9%

Dependent repos count: 57.5%

Maintainers (1)

ErnisMeshi

Last synced: 4 months ago

pypi.org: ultralytics-export-to-rknn

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Documentation: https://ultralytics-export-to-rknn.readthedocs.io/
License: AGPL-3.0
Latest release: 8.2.88
published over 1 year ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 28 Last month

Rankings

Dependent packages count: 10.4%

Average: 34.4%

Dependent repos count: 58.4%

Maintainers (1)

ErnisMeshi

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: ultralytics-rknn-export

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Documentation: https://ultralytics-rknn-export.readthedocs.io/
License: AGPL-3.0

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 137 Last month

Rankings

Dependent packages count: 10.4%

Average: 34.4%

Dependent repos count: 58.4%

Maintainers (1)

ErnisMeshi

Funding

https://ultralytics.com

Last synced: about 1 year ago

pypi.org: ultralytics-yolov10-rknn

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Documentation: https://ultralytics-yolov10-rknn.readthedocs.io/
License: AGPL-3.0
Latest release: 8.1.34
published over 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 27 Last month

Rankings

Dependent packages count: 10.4%

Average: 34.4%

Dependent repos count: 58.5%

Maintainers (1)

ErnisMeshi

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: ymsyolo10

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Documentation: https://ymsyolo10.readthedocs.io/
License: AGPL-3.0
Latest release: 8.1.34
published over 1 year ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 20 Last month

Rankings

Dependent packages count: 10.4%

Average: 34.5%

Dependent repos count: 58.6%

Maintainers (1)

3125017yms

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: ultralytics-rknn

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://ultralytics-rknn.readthedocs.io/
License: AGPL-3.0
Latest release: 12.0.1
published over 1 year ago

Versions: 18
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 79 Last month

Rankings

Dependent packages count: 9.6%

Average: 36.3%

Dependent repos count: 63.1%

Maintainers (1)

ErnisMeshi

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: yolov8-rknn

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://yolov8-rknn.readthedocs.io/
License: AGPL-3.0
Latest release: 8.0.151
published over 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 27 Last month

Rankings

Dependent packages count: 9.6%

Average: 36.3%

Dependent repos count: 63.1%

Maintainers (1)

ErnisMeshi

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: yolov8-pose-triton

Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Documentation: https://yolov8-pose-triton.readthedocs.io/
License: AGPL-3.0
Latest release: 8.2.0
published over 1 year ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 74 Last month

Rankings

Dependent packages count: 10.0%

Forks count: 29.8%

Average: 36.5%

Stargazers count: 38.8%

Dependent repos count: 67.4%

Maintainers (1)

hmurari

Funding

https://ultralytics.com

Last synced: 4 months ago

pypi.org: simplelatexocr

A simple LaTeX OCR package

Documentation: https://simplelatexocr.readthedocs.io/
License: Apache-2.0
Latest release: 0.0.1
published almost 2 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 9.7%

Average: 37.0%

Dependent repos count: 64.2%

Maintainers (1)

chaodreaming

Last synced: about 1 year ago

pypi.org: ultralyticsutils

My short description for my project.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://ultralyticsutils.readthedocs.io/
License: MIT
Latest release: 8.0.5
published over 1 year ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 47 Last month

Rankings

Dependent packages count: 9.9%

Average: 37.8%

Dependent repos count: 65.7%

Maintainers (1)

advance

Last synced: 4 months ago

pypi.org: xn-ultralytics

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.

Homepage: https://github.com/ultralytics/ultralytics
Documentation: https://xn-ultralytics.readthedocs.io/
License: AGPL-3.0
Latest release: 8.0.119
published over 2 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 19 Last month

Rankings

Dependent packages count: 7.4%

Average: 38.1%

Dependent repos count: 68.8%

Maintainers (1)

beyse

Funding

https://ultralytics.com

Last synced: 4 months ago

Dependencies

.github/workflows/ci.yaml actions

actions/cache v3 composite
actions/checkout v3 composite
actions/setup-python v4 composite

.github/workflows/cla.yml actions

contributor-assistant/github-action v2.2.0 composite

.github/workflows/docker.yaml actions

actions/checkout v3 composite
docker/build-push-action v3 composite
docker/login-action v2 composite
docker/setup-buildx-action v2 composite
docker/setup-qemu-action v2 composite

docker/Dockerfile docker

nvcr.io/nvidia/pytorch 22.12-py3 build

requirements.txt pypi

GitPython >=3.1.24
Pillow >=7.1.2
PyYAML >=5.3.1
hydra-core >=1.2.0
ipython *
matplotlib >=3.2.2
numpy >=1.18.5
opencv-python >=4.1.1
pandas >=1.1.4
psutil *
requests >=2.23.0
scipy >=1.4.1
seaborn >=0.11.0
tensorboard >=2.4.1
thop >=0.1.1
torch >=1.7.0
torchvision >=0.8.1
tqdm >=4.64.0

.github/workflows/codeql.yaml actions

actions/checkout v4 composite
github/codeql-action/analyze v2 composite
github/codeql-action/init v2 composite

.github/workflows/greetings.yml actions

actions/first-interaction v1 composite

.github/workflows/links.yml actions

actions/checkout v4 composite
nick-invision/retry v2 composite

.github/workflows/publish.yml actions

actions/checkout v4 composite
actions/setup-python v4 composite
slackapi/slack-github-action v1.24.0 composite

.github/workflows/stale.yml actions

actions/stale v8 composite

setup.py pypi