<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.
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
1、yolov8行人横道检测,yolov8斑马线检测;包含训练好的yolov8行人横道检测权重以及PR曲线,loss曲线等等,map达90% 多,yolov8行人横道检测数据集中训练得到的权重; 2、数据集和检测结果参考:https://blog.csdn.net/zhiqingAI/article/details/124230743 3、采用pytrch框架,python代码,可以和YOLOv5共用一个环境,配置好环境就可以加载已经训练好的模型直接进行测试,得出结果 4、https://blog.csdn.net/zhiqingAI/article/details/134629857
资源推荐
资源详情
资源评论
收起资源包目录
yolov8人行横道检测权重 yolov8斑马线检测 (1253个子文件)
events.out.tfevents.1704079625.USER-20231125JB.14600.0 384KB
events.out.tfevents.1704089519.USER-20231125JB.7160.0 371KB
CITATION.cff 612B
setup.cfg 2KB
CNAME 21B
inference.cpp 12KB
inference.cpp 6KB
main.cpp 4KB
main.cpp 2KB
style.css 1KB
results.csv 40KB
results.csv 33KB
Dockerfile 4KB
Dockerfile-arm64 2KB
Dockerfile-conda 2KB
Dockerfile-cpu 2KB
Dockerfile-jetson 2KB
Dockerfile-python 2KB
Dockerfile-runner 2KB
.gitignore 2KB
inference.h 2KB
inference.h 2KB
comments.html 2KB
source-file.html 858B
favicon.ico 9KB
MANIFEST.in 200B
tutorial.ipynb 33KB
hub.ipynb 4KB
train_batch2.jpg 516KB
train_batch2.jpg 516KB
train_batch0.jpg 503KB
train_batch0.jpg 503KB
train_batch27612.jpg 441KB
train_batch1.jpg 437KB
train_batch1.jpg 437KB
val_batch0_pred.jpg 422KB
val_batch0_pred.jpg 422KB
val_batch0_labels.jpg 418KB
val_batch0_labels.jpg 418KB
train_batch27610.jpg 388KB
train_batch22591.jpg 379KB
train_batch22592.jpg 371KB
train_batch27611.jpg 367KB
val_batch1_pred.jpg 367KB
val_batch1_pred.jpg 367KB
val_batch1_labels.jpg 363KB
val_batch1_labels.jpg 363KB
val_batch2_pred.jpg 353KB
val_batch2_pred.jpg 353KB
train_batch22590.jpg 348KB
val_batch2_labels.jpg 347KB
val_batch2_labels.jpg 347KB
labels_correlogram.jpg 188KB
labels_correlogram.jpg 188KB
labels.jpg 156KB
labels.jpg 156KB
01_4885_filename1469_jpg.rf.fe1e1a3c80a24526518931a6371e2d51.jpg 148KB
01_4885_filename1566_jpg.rf.7f42b7a22af902672a05d3f5b8469f28.jpg 147KB
01_4885_filename1546_jpg.rf.1cea230e5f428d57564bc5c096c1a05a.jpg 147KB
01_4885_filename1565_jpg.rf.c5f1faaa0cc8803a8fb4230c53688da5.jpg 146KB
01_4885_filename1457_jpg.rf.7eb2e6e8fedece08ea4e087259aba204.jpg 145KB
01_4885_filename1539_jpg.rf.8d057dc7683bee8f3344fc838838909d.jpg 138KB
bus.jpg 134KB
03_0760_filename1049_jpg.rf.ed691067f49376d50db62d17af82cb85.jpg 129KB
01_4885_filename3739_jpg.rf.55cb7756dd31ded05312189cb1583839.jpg 129KB
01_4885_filename3714_jpg.rf.95fc1c2bb92e915fbcffd7ad67442768.jpg 128KB
03_0760_filename0840_jpg.rf.c085f11f106462e8da9f84b700bb626a.jpg 127KB
03_0760_filename0852_jpg.rf.3ef00a28fd93403630285fd2a332037c.jpg 123KB
01_4885_filename0180_jpg.rf.6d3a401862453404c31c8a9a2e994538.jpg 120KB
03_0760_filename1052_jpg.rf.1bfd6c61a6647e835ae1a014d1d14dd8.jpg 119KB
03_0760_filename1058_jpg.rf.d8f9e77748501e659fce2b334baa2272.jpg 119KB
03_0760_filename0008_jpg.rf.2deb039f53b71598822ee36296b965a2.jpg 119KB
03_0760_filename0087_jpg.rf.ce661203bd06cc190172817e6d1a3ae0.jpg 119KB
03_0760_filename0006_jpg.rf.6ceb914ce615d99411c3935b34079d2a.jpg 119KB
03_0760_filename0059_jpg.rf.86ac03e50ff7dff4da6e27842cbc20ec.jpg 119KB
03_0760_filename0034_jpg.rf.18185e8b60633f75d74664bd2486e9db.jpg 118KB
01_4885_filename3544_jpg.rf.279b0c5e35555726b629fdec774de53b.jpg 118KB
03_0760_filename0107_jpg.rf.2ffa9296b1b832b9ab12f5bf119ac873.jpg 118KB
03_0760_filename0073_jpg.rf.f2f1f1476a074916af3046c8778917b4.jpg 118KB
01_4885_filename3545_jpg.rf.163d117eb1d05bc1e9206e1420ed3c2e.jpg 118KB
03_0760_filename0092_jpg.rf.6037f5b3b9849f762404b37eccc77935.jpg 117KB
03_0760_filename0174_jpg.rf.0bd07917538baf0ecde89977c682985e.jpg 117KB
01_4885_filename0249_jpg.rf.3edaf64d3039eea7cd0d647317f3648d.jpg 117KB
03_0760_filename0090_jpg.rf.e0a5a3bd4c1cf8a4642f42fcaccdbad7.jpg 117KB
01_4885_filename2492_jpg.rf.c515e90486eeab289168abe8346f1a30.jpg 116KB
03_0760_filename0140_jpg.rf.a1865eaa60c8d81bf485a3f11f904964.jpg 115KB
03_0760_filename0152_jpg.rf.18b4686b81c50df2c2741d4b8454e1e9.jpg 115KB
03_0760_filename2665_jpg.rf.9c6ee767680ec8483f89ccbb62338a98.jpg 114KB
03_0760_filename0869_jpg.rf.f728262be70bd83493919c2e8ee0a935.jpg 114KB
03_0760_filename0869_jpg.rf.71abd69743872882a97ff1e8e8ea5db8.jpg 114KB
03_0760_filename0126_jpg.rf.08b44e4ed50cd15986f45a1aba4f4d96.jpg 113KB
02_0036_filename5563_jpg.rf.3a9faa2d5bd0b317a0face6c022d8ad1.jpg 112KB
02_0036_filename5508_jpg.rf.ebe69e1212335e96588308f39e8653e2.jpg 112KB
02_0036_filename5495_jpg.rf.6a054ae1cdc7caa05a29fb8c3a04edab.jpg 112KB
02_0036_filename5557_jpg.rf.109fe518829a967e4b7ba4f85ea9db0f.jpg 111KB
02_0036_filename5537_jpg.rf.30d865011ace4a1204236c849ab9901a.jpg 111KB
02_0036_filename5447_jpg.rf.a458ab385233f03902a8e4cec275414f.jpg 110KB
03_0760_filename0870_jpg.rf.5740cb7fe71e7cd2bc124c8424a2909a.jpg 110KB
02_0036_filename5486_jpg.rf.4154a28e0a7e483db83b68ec3c330fe7.jpg 110KB
02_0036_filename5447_jpg.rf.d5cbca6d5bacd5b12c6097ebcd65315f.jpg 110KB
共 1253 条
- 1
- 2
- 3
- 4
- 5
- 6
- 13
资源评论
- weixin_570587082024-03-29资源不错,内容挺好的,有一定的使用价值,值得借鉴,感谢分享。
stsdddd
- 粉丝: 2w+
- 资源: 686
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