<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resnet】对水果目标检测数据集,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:249MB 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0.96,map0.5:0.95=0.59。这里仅仅训练了30个epoch,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 【数据集介绍】水果图像数据,5类别:番茄、核桃、橘子、龙眼、青枣 训练集datasets-images-train:216张图片和216个标签txt文件组成 验证集datasets-images-val:54张图片和54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resnet】:水果检测(5类别) (780个子文件)
events.out.tfevents.1712301182.sdxx-System-Product-Name.6306.0 1.41MB
CITATION.cff 393B
results.csv 9KB
Dockerfile 3KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
.dockerignore 4KB
.gitattributes 75B
.gitignore 4KB
tutorial.ipynb 101KB
tutorial.ipynb 42KB
tutorial.ipynb 40KB
IMG20211212115836_1.jpg 3.91MB
IMG20211212115836_2.jpg 3.9MB
IMG20211212115837.jpg 3.9MB
IMG20211212115820.jpg 3.88MB
IMG20211212115836_1.jpg 3.82MB
IMG20211212115536.jpg 3.73MB
IMG20211212115534.jpg 3.7MB
IMG20211212115534.jpg 3.57MB
IMG20211212115851.jpg 3.34MB
IMG20211212115819.jpg 3.34MB
IMG20211212115532.jpg 3.33MB
IMG20211212115521.jpg 3.29MB
IMG20211212125156.jpg 3.29MB
IMG20211212115851.jpg 3.27MB
IMG20211212115520.jpg 3.25MB
IMG20211212115554_1.jpg 3.24MB
IMG20211212115552.jpg 3.24MB
IMG20211212115532.jpg 3.22MB
IMG20211212115554.jpg 3.22MB
IMG20211212115548.jpg 3.2MB
IMG20211212115520.jpg 3.17MB
IMG20211212115459.jpg 3.01MB
IMG20211212115503.jpg 2.99MB
IMG20211212115502.jpg 2.99MB
IMG20211212115500.jpg 2.99MB
IMG20211212115459.jpg 2.96MB
IMG20211212115852.jpg 2.91MB
IMG20211212115901.jpg 2.89MB
IMG20211212115518.jpg 2.89MB
IMG20211212115506.jpg 2.89MB
IMG20211212115902.jpg 2.85MB
IMG20211212115852.jpg 2.83MB
IMG20211212115518.jpg 2.8MB
IMG20211212115542.jpg 2.7MB
IMG20211212115530.jpg 2.68MB
IMG20211212115539.jpg 2.65MB
IMG20211212115538.jpg 2.64MB
IMG20211212115821.jpg 2.62MB
IMG20211212115553.jpg 2.62MB
IMG20211212115548_1.jpg 2.58MB
IMG20211212115542_1.jpg 2.57MB
IMG20211212115542.jpg 2.54MB
IMG20211212115538.jpg 2.52MB
IMG20211212115548_1.jpg 2.47MB
bus.jpg 476KB
val_batch0_pred.jpg 437KB
val_batch2_pred.jpg 430KB
val_batch0_labels.jpg 406KB
val_batch1_pred.jpg 405KB
val_batch2_labels.jpg 405KB
val_batch1_labels.jpg 382KB
train_batch0.jpg 319KB
train_batch1.jpg 318KB
train_batch2.jpg 282KB
labels_correlogram.jpg 212KB
zidane.jpg 165KB
labels.jpg 161KB
244.jpg 127KB
248.jpg 121KB
221.jpg 118KB
231.jpg 115KB
193.jpg 114KB
218.jpg 106KB
197.jpg 101KB
124.jpg 97KB
101.jpg 96KB
98.jpg 96KB
86.jpg 95KB
264.jpg 94KB
169.jpg 94KB
163.jpg 92KB
258.jpg 92KB
177.jpg 92KB
104.jpg 86KB
135.jpg 86KB
96.jpg 84KB
102.jpg 83KB
210.jpg 83KB
82.jpg 82KB
107.jpg 80KB
37.jpg 80KB
125.jpg 79KB
59.jpg 79KB
92.jpg 79KB
240.jpg 79KB
155.jpg 76KB
13.jpg 73KB
共 780 条
- 1
- 2
- 3
- 4
- 5
- 6
- 8
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
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