<div align="center">
<p>
<a href="http://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/yolov8/banner-yolov8.png"></a>
<!--
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png"></a>
-->
</p>
[涓枃](https://docs.ultralytics.com/zh/) | [頃滉淡鞏碷(https://docs.ultralytics.com/ko/) | [鏃ユ湰瑾瀅(https://docs.ultralytics.com/ja/) | [袪褍褋褋泻懈泄](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Fran莽ais](https://docs.ultralytics.com/fr/) | [Espa帽ol](https://docs.ultralytics.com/es/) | [Portugu锚s](https://docs.ultralytics.com/pt/) | [啶灌た啶ㄠ啶︵](https://docs.ultralytics.com/hi/) | [丕賱毓乇亘賷丞](https://docs.ultralytics.com/ar/)
<div>
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
<br>
YOLOv5 馃殌 is the world's most loved vision AI, representing <a href="https://ultralytics.com">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href="https://docs.ultralytics.com/yolov5">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/yolov5/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).
<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>
<br>
## <div align="center">YOLOv8 馃殌 NEW</div>
We are thrilled to announce the launch of Ultralytics YOLOv8 馃殌, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. 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.
See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with:
[![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
```
<div align="center">
<a href="https://ultralytics.com/yolov8" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for full documentation on training, testing and deployment. See below for quickstart examples.
<details open>
<summary>Install</summary>
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
```bash
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
```
</details>
<details>
<summary>Inference</summary>
YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom
# Images
img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
</details>
<details>
<summary>Inference with detect.py</summary>
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
python detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
YOLOV5 改进实战项目【更换骨干网络为shufflenet】对CT图像肾脏、结石检测,包含代码、数据集、训练好的权重参数,经测试,代码可以直接使用。 【yolov5】项目总大小:68 MB 本项目更换了yolov5骨干网络为官方实现的resnet网络,简单训练了30个epoch,map指标为0.56,map0.5:0.95=0.23。这里仅仅训练了30个epoch用于测试,网络还没收敛,加大轮次可以获取更高的网络性能 【如何训练】和yolov5一样的训练方法,摆放好datasets数据,然后更改yaml文件中的类别信息即可训练 【数据集】(数据分为分为训练集和验证集) 训练集datasets-images-train:325张图片和325个标签txt文件组成 验证集datasets-images-val:135张图片和135个标签txt文件组成 更多yolov5改进介绍、或者如何训练,请参考: https://blog.csdn.net/qq_44886601/category_12605353.html
资源推荐
资源详情
资源评论
收起资源包目录
YOLOV5 改进实战项目【更换骨干网络为shufflenet】:CT图像肾脏、结石检测(包含数据、代码、训练好的权重文件 (1259个子文件)
events.out.tfevents.1714050795.sdxx-System-Product-Name.27642.0 1.46MB
CITATION.cff 393B
results.csv 9KB
Dockerfile 3KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
.dockerignore 4KB
.gitattributes 75B
.gitignore 4KB
.gitignore 50B
yolov5.iml 452B
tutorial.ipynb 101KB
tutorial.ipynb 42KB
tutorial.ipynb 40KB
val_batch2_pred.jpg 600KB
val_batch1_pred.jpg 588KB
val_batch2_labels.jpg 584KB
val_batch0_pred.jpg 577KB
val_batch0_labels.jpg 563KB
val_batch1_labels.jpg 555KB
train_batch1.jpg 498KB
train_batch0.jpg 489KB
bus.jpg 476KB
train_batch2.jpg 455KB
labels_correlogram.jpg 199KB
zidane.jpg 165KB
labels.jpg 114KB
Stone-97-_jpg.rf.1da359791431a08866fc9bc45543bbff.jpg 108KB
Stone-699-_jpg.rf.97fcee66a7c6601d43fef3b5ee9df023.jpg 106KB
Stone-98-_jpg.rf.32a172646a4501115c33455c855b97f3.jpg 106KB
Stone-694-_jpg.rf.3600cbdcc713efccba4393cbc0218396.jpg 105KB
Stone-695-_jpg.rf.a7b047d2d0be299d150333af53382cbb.jpg 104KB
Stone-708-_jpg.rf.466e0a13e7bb07eb446f677fc1bc1f1e.jpg 103KB
Stone-704-_jpg.rf.126844d1883d78e6d1944795d8b7bf4d.jpg 101KB
Stone-96-_jpg.rf.75723dbb6fa1049cf4a4a18eaf06e4ea.jpg 101KB
Stone-698-_jpg.rf.bb061fedc493e2891511718a880d0d29.jpg 101KB
Stone-701-_jpg.rf.e2a2c18114a09a5c00099f7758f3805f.jpg 100KB
Stone-719-_jpg.rf.3daa5f39e744976296bf38fa97af7c87.jpg 99KB
Stone-720-_jpg.rf.d5d6bd6468025d71a3a9002010f0e7ea.jpg 99KB
Stone-696-_jpg.rf.5cf6592f3d3090d65d96d2c97b3a816d.jpg 98KB
Stone-83-_jpg.rf.054908309e8a6d2e935d5146eb015753.jpg 97KB
Stone-855-_jpg.rf.119b49f461eca4438c2b3f8799020b7a.jpg 97KB
Stone-891-_jpg.rf.871a4ee18a4bde9d43a27c06f34f1fe6.jpg 96KB
Stone-85-_jpg.rf.070dcb7349dd7b2b77dcba29e27fc231.jpg 96KB
Stone-890-_jpg.rf.4cdb83e21c26031c1f4d1be307a2028a.jpg 96KB
Stone-84-_jpg.rf.b9d554ae8a706de388c2d3b6bfdf851e.jpg 96KB
Stone-76-_jpg.rf.21b0b22d6f21d8842ceb6ac27632f97d.jpg 95KB
Stone-850-_jpg.rf.3c3122a92c6b6788db50e6efdc96ca4c.jpg 94KB
Stone-566-_jpg.rf.f5df7974d1f9450fd9334be7d6b74681.jpg 94KB
Stone-72-_jpg.rf.182ec7f9608885b6d2ced573821c5faf.jpg 94KB
Stone-65-_jpg.rf.bb9f746cb7e1dd608e97c709635c3c83.jpg 93KB
Stone-646-_jpg.rf.adb67f58fb06f68dff7f7943fa9a1d57.jpg 92KB
Stone-824-_jpg.rf.0cce45e286b41d27f918fb35ccc44fcd.jpg 92KB
Stone-861-_jpg.rf.11fa5fe16fb7f20327f5c38d1e72edee.jpg 92KB
Stone-664-_jpg.rf.06875cf5fc60736baa426fa481f40b41.jpg 92KB
Stone-810-_jpg.rf.1e570ad2e8d507e7d3e297c92621d733.jpg 92KB
Stone-644-_jpg.rf.3cbc5d45a1ec149e3844885201febaf4.jpg 92KB
Stone-895-_jpg.rf.a8189a6f41869319192cdea2707f6261.jpg 92KB
Stone-893-_jpg.rf.bb70066eec2dd012bc5fe5353c12a58d.jpg 92KB
Stone-614-_jpg.rf.8b153e3e8978ee3b99d3ffeb0b75bf18.jpg 92KB
Stone-676-_jpg.rf.1048f01c0bc657c7a67ffc36cfa287a9.jpg 91KB
Stone-643-_jpg.rf.f548b417225c582d6e9ff4a206c66432.jpg 91KB
Stone-983-_jpg.rf.ca65b642782a1cf54f9c3b383e451ec8.jpg 91KB
Stone-681-_jpg.rf.372882c4138251b770186e22ea9cfb6a.jpg 91KB
Stone-673-_jpg.rf.808f89383dbc20788dc88c9cca096420.jpg 91KB
Stone-653-_jpg.rf.254de70d9a660a7a0ceaa81b49634dc1.jpg 91KB
Stone-985-_jpg.rf.b4467471572b2e55031fa47632c1ddc1.jpg 91KB
Stone-648-_jpg.rf.41d6a274af6e1181f7a7a1f5642ae3ae.jpg 91KB
Stone-686-_jpg.rf.e06d74c5db6454df9e7667f3847a5aa8.jpg 91KB
Stone-671-_jpg.rf.4667bdf41d7962e4ca12b266291ee4ea.jpg 91KB
Stone-863-_jpg.rf.68fde98aee0d01b10d542ee4fd027b10.jpg 91KB
Stone-650-_jpg.rf.4ca6a745f2ba065ccf865e239d2a6f5f.jpg 90KB
Stone-727-_jpg.rf.9e6cc7c438b122ca5773f81a16bbb993.jpg 90KB
Stone-640-_jpg.rf.90ab638fe79154dee8984c3b1f8ec967.jpg 90KB
Stone-642-_jpg.rf.e73e3a2f6e8894e7f5a8bf8b1059966e.jpg 90KB
Stone-632-_jpg.rf.551207690d97d5906eefc9a0d1e6a692.jpg 89KB
Stone-822-_jpg.rf.d24e4841d18cee5d62b713da8ed06567.jpg 89KB
Stone-818-_jpg.rf.55543034de26cc123aa613f6e99acf8e.jpg 89KB
Stone-623-_jpg.rf.aa57f70883eec27b3390665527ff39ea.jpg 89KB
Stone-636-_jpg.rf.bded5d01ce040c03345f96bc436c3940.jpg 89KB
Stone-579-_jpg.rf.1970777a328817b7b7e08372119fd434.jpg 89KB
Stone-624-_jpg.rf.2318a9c9c1f8dff30a0ca1fb187a7d76.jpg 89KB
Stone-823-_jpg.rf.8404f8e84083d41c4136e1374ad5dd01.jpg 89KB
Stone-569-_jpg.rf.db7b61ca5bd38127a31b5856d6c2f3f4.jpg 88KB
Stone-633-_jpg.rf.a5fd70f199088994f1715a3fd9d3dd04.jpg 88KB
Stone-600-_jpg.rf.d19b7327b2f441ce3e2bbb3e567039e7.jpg 88KB
Stone-615-_jpg.rf.16c7ad7ab4b69feafc5c768473661ece.jpg 88KB
Stone-726-_jpg.rf.ab6b4fbe5a919a8d0213635875732204.jpg 87KB
Stone-598-_jpg.rf.d0de4a1ac0c7852ca72c29909e263642.jpg 87KB
Stone-58-_jpg.rf.7759222c2db094076d7947ce181a7101.jpg 86KB
Stone-564-_jpg.rf.0b35b34ab38c71b7cba8df7c7b9dafe2.jpg 86KB
Stone-987-_jpg.rf.40ad730b34e92ad95732398d06ab61e2.jpg 86KB
Stone-594-_jpg.rf.269570938ed568ae48685fc5370a6511.jpg 86KB
Stone-61-_jpg.rf.824acc8c09090c61f64dee18a5fb0140.jpg 86KB
Stone-60-_jpg.rf.725166134f58bd3a0e8a778cfecab78b.jpg 85KB
Stone-761-_jpg.rf.f518afb617d55fb340785f9b5d2c6c2e.jpg 85KB
Stone-759-_jpg.rf.6a6300c96c52c24451b976b88ab2913b.jpg 84KB
Stone-762-_jpg.rf.e062a602c7774e6c96f1479bd708c681.jpg 84KB
Stone-997-_jpg.rf.3944c235dc2ce23f798a42f9d54166bc.jpg 84KB
共 1259 条
- 1
- 2
- 3
- 4
- 5
- 6
- 13
资源评论
听风吹等浪起
- 粉丝: 1w+
- 资源: 1242
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
最新资源
- 937712277954201实习5.word
- 2程序语言基础知识pdf1_1716337722703.jpeg
- 简单的Python示例,演示了如何使用TCP/IP协议进行基本的客户端和服务器通信
- 考试.sql
- keil2 + proteus + 8051.exe
- 1961ee27df03bd4595d28e24b00dde4e_744c805f7e4fb4d40fa3f695bfbab035_8(1).c
- mediapipe-0.9.0.1-cp37-cp37m-win-amd64.whl.zip
- windows注册表编辑工具
- mediapipe-0.9.0.1-cp37-cp37m-win-amd64.whl.zip
- 校园通行码预约管理系统20240522075502
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
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