<div align="center">
<p>
<a href="https://www.ultralytics.com/blog/ultralytics-yolov8-turns-one-a-year-of-breakthroughs-and-innovations" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png" alt="YOLO Vision banner"></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>
<a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></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" alt="YOLOv8 performance plots"></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%" alt="space">
<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%" alt="space">
<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%" alt="space">
<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%" alt="space">
<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%" alt="space">
<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%" alt="space">
<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/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://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>
### Notebooks
Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics) tutorial, making it easy to learn and implement advanced YOLOv8 features.
| Docs
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
YOLOv8是一种改进的You Only Look Once(YOLO)目标检测算法,而PyTorch则是一个常用的深度学习框架。虽然原始版本的YOLO通常使用Darknet框架实现,但是可以通过将YOLOv8模型迁移到PyTorch框架中来实现。 以下是在PyTorch中实现YOLOv8的一般步骤: 1. **了解YOLOv8模型结构**: - 首先,要深入了解YOLOv8算法的改进和结构,以便在PyTorch中重新构建该模型。 - YOLOv8通常包含类似Darknet中的Backbone网络(如CSPDarknet53)、FPN(Feature Pyramid Network)、YOLO头等组件。 2. **转换模型结构为PyTorch代码**: - 基于YOLOv8的论文和公开实现代码,将模型结构转换为PyTorch代码。创建对应的网络层、损失函数和模块,并组合它们以构建完整的YOLOv8模型。 3. **加载权重和预训练模型**: - 可能需要将从Darknet权重中提取的参数导入到PyTorch模型中,以便从预训练的模型中初始化权重。....
资源推荐
资源详情
资源评论
收起资源包目录
YOLOv8 在PyTorch中 (561个子文件)
main.cc 10KB
CITATION.cff 609B
CNAME 21B
inference.cpp 13KB
inference.cpp 6KB
main.cpp 5KB
main.cpp 2KB
style.css 1KB
Dockerfile 4KB
Dockerfile-arm64 2KB
Dockerfile-conda 2KB
Dockerfile-cpu 3KB
Dockerfile-jetson 2KB
Dockerfile-python 2KB
Dockerfile-runner 2KB
.gitignore 2KB
inference.h 2KB
inference.h 2KB
comments.html 2KB
source-file.html 858B
main.html 433B
favicon.ico 9KB
tutorial.ipynb 33KB
explorer.ipynb 22KB
object_tracking.ipynb 8KB
object_counting.ipynb 6KB
heatmaps.ipynb 6KB
hub.ipynb 4KB
bus.jpg 134KB
zidane.jpg 49KB
extra.js 3KB
LICENSE 34KB
predict.md 47KB
cfg.md 41KB
README.md 36KB
README.zh-CN.md 35KB
train.md 28KB
model-deployment-options.md 23KB
yolov8.md 20KB
openvino.md 20KB
quickstart.md 19KB
yolo-common-issues.md 17KB
train_custom_data.md 17KB
track.md 16KB
roboflow.md 16KB
model_export.md 15KB
heatmaps.md 15KB
isolating-segmentation-objects.md 15KB
inference-api.md 14KB
pytorch_hub_model_loading.md 14KB
simple-utilities.md 14KB
README.md 13KB
sam.md 13KB
kfold-cross-validation.md 12KB
python.md 12KB
architecture_description.md 12KB
pose.md 12KB
obb.md 12KB
api.md 12KB
segment.md 12KB
yolo-world.md 12KB
yolo-performance-metrics.md 11KB
CI.md 11KB
multi_gpu_training.md 11KB
projects.md 11KB
classify.md 11KB
detect.md 11KB
hyperparameter_evolution.md 11KB
clearml_logging_integration.md 11KB
ray-tune.md 11KB
comet_logging_integration.md 11KB
neural_magic_pruning_quantization.md 11KB
test_time_augmentation.md 11KB
yolov5.md 11KB
tensorboard.md 10KB
object-counting.md 10KB
amazon-sagemaker.md 10KB
clearml.md 10KB
model_ensembling.md 10KB
android.md 10KB
running_on_jetson_nano.md 10KB
weights-biases.md 10KB
index.md 10KB
fast-sam.md 10KB
hyperparameter-tuning.md 10KB
torchscript.md 10KB
cli.md 9KB
yolov9.md 9KB
index.md 9KB
dvc.md 9KB
export.md 9KB
neural-magic.md 9KB
index.md 9KB
comet.md 9KB
model_pruning_and_sparsity.md 9KB
datasets.md 8KB
index.md 8KB
workouts-monitoring.md 8KB
raspberry-pi.md 8KB
index.md 8KB
共 561 条
- 1
- 2
- 3
- 4
- 5
- 6
资源评论
专家-百锦再
- 粉丝: 7427
- 资源: 731
下载权益
C知道特权
VIP文章
课程特权
开通VIP
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功