<div align="center">
<p>
<a href="https://yolovision.ultralytics.com/" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
</p>
[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/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
<br>
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://codecov.io/github/ultralytics/ultralytics"><img src="https://codecov.io/github/ultralytics/ultralytics/branch/main/graph/badge.svg?token=HHW7IIVFVY" alt="Ultralytics Code Coverage"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
<br>
[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/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 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 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
</div>
</div>
## <div align="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
<details open>
<summary>Install</summary>
Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/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 Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
</details>
<details open>
<summary>Usage</summary>
#### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
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](https://docs.ultralytics.com/usage/cli) for examples.
#### Python
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
```python
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
```
See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
</details>
## <div align="center">Models</div>
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="Ultralytics YOLO supported tasks">
All [Models](https://github.
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YOLOv8烟雾检测代码+训练好的smoke模型+烟雾数据集
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txt:1283个
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py:134个
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1、YOLOv8训练好的烟雾检测模型,并包含4500多张标注好的烟雾数据集,标签格式为xml和txt两种,类别名为smoke,配置好YOLOv8环境后可以直接使用,含qt界面 2、数据集和检测结果参考:https://blog.csdn.net/zhiqingAI/article/details/124230743 4、采用pytrch框架,代码是python的
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YOLOv8烟雾检测代码+训练好的smoke模型+烟雾数据集 (2000个子文件)
inference.cpp 12KB
inference.cpp 6KB
main.cpp 4KB
main.cpp 2KB
style.css 1KB
inference.h 2KB
inference.h 2KB
comments.html 2KB
source-file.html 858B
extra.js 3KB
predict.md 40KB
train.md 38KB
predict.md 36KB
quickstart.md 34KB
track.md 31KB
README.md 28KB
README.zh-CN.md 27KB
train.md 25KB
sam.md 25KB
yolov8.md 24KB
predict.md 23KB
quickstart.md 23KB
model-deployment-options.md 23KB
yolov8.md 23KB
cfg.md 23KB
sam.md 22KB
yolov8.md 21KB
track.md 21KB
openvino.md 20KB
yolov8.md 19KB
yolov8.md 19KB
yolov8.md 19KB
yolov8.md 19KB
index.md 19KB
fast-sam.md 19KB
yolov8.md 19KB
predict.md 19KB
quickstart.md 19KB
yolov8.md 18KB
train.md 18KB
pose.md 18KB
yolov8.md 18KB
sam.md 17KB
yolov8.md 17KB
yolo-common-issues.md 17KB
segment.md 17KB
train_custom_data.md 17KB
quickstart.md 17KB
classify.md 17KB
track.md 16KB
detect.md 16KB
train.md 16KB
roboflow.md 16KB
index.md 16KB
yolov5.md 16KB
track.md 16KB
sam.md 16KB
segment.md 15KB
predict.md 15KB
fast-sam.md 15KB
train.md 15KB
sam.md 15KB
pose.md 15KB
model_export.md 15KB
sam.md 15KB
yolov5.md 15KB
detect.md 15KB
index.md 15KB
classify.md 14KB
sam.md 14KB
predict.md 14KB
inference_api.md 14KB
predict.md 14KB
quickstart.md 14KB
segment.md 14KB
predict.md 14KB
pytorch_hub_model_loading.md 14KB
yolov4.md 14KB
predict.md 14KB
sam.md 14KB
pose.md 14KB
predict.md 14KB
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sam.md 14KB
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predict.md 13KB
detect.md 13KB
segment.md 13KB
pose.md 13KB
index.md 13KB
pose.md 13KB
README.md 13KB
track.md 13KB
classify.md 13KB
pose.md 13KB
segment.md 13KB
segment.md 13KB
pose.md 13KB
pose.md 13KB
models.md 13KB
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