<div align="center">
<p>
<a href="https://ultralytics.com/yolov8" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
</p>
[English](README.md) | [简体中文](README.zh-CN.md)
<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://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">GitHub</a> for support, and join our <a href="https://discord.gg/n6cFeSPZdD">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>
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<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
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<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a>
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<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></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>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
```bash
pip install ultralytics
```
</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
success = model.export(format="onnx") # export the model to ONNX format
```
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
</details>
## <div align="center">Models</div>
All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
<details open><summary>Detection</summary>
See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.
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计算机毕业设计:基于YOLOv8实现的火焰识别监测系统(源码+说明),保证可靠运行,附赠计算机答辩PPT模板
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《计算机毕业设计:基于YOLOv8实现的火焰识别监测系统》是一项富有创新性和实用价值的计算机毕业设计项目。该项目以深度学习技术为基础,利用YOLOv8算法实现火焰的准确识别与监测,为火灾预防和安全管理领域提供了新的解决方案。 该系统以YOLOv8算法为核心,通过训练和优化模型,使其能够在各种复杂环境中准确识别火焰。YOLOv8算法以其高效的检测速度和优秀的识别精度而闻名,为火焰识别监测系统提供了强大的技术支持。 在项目实施过程中,该设计充分考虑了实际应用场景的需求,通过调整算法参数和优化模型结构,提高了火焰识别的准确性和稳定性。同时,系统还具备实时监测和报警功能,一旦检测到火焰,便会立即触发报警机制,提醒相关人员及时采取应对措施。 此外,该项目还提供了完整的源码和详细的说明文档。源码编写规范,注释清晰,方便学习者理解和掌握系统的实现原理。说明文档则对项目的背景、目标、实现过程以及技术难点进行了全面阐述,为学习者提供了宝贵的参考资料。 为了方便学习者进行毕业设计答辩,该项目还附赠了计算机答辩PPT模板。模板设计专业、简洁,内容结构清晰,能够充分展示项目的创新点和实践成果。
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计算机毕业设计:基于YOLOv8实现的火焰识别监测系统(源码+说明),保证可靠运行,附赠计算机答辩PPT模板 (2000个子文件)
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