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<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>
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<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).
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<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
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温馨提示
YOLOV5 改进实战项目【更换骨干网络为resnet】对橘子是否成熟检测,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:196 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0.98,map0.5:0.95=0.68。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:2313张图片和2313个标签txt文件组成 验证集datasets-images-val:224张图片和224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
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YOLOV5 改进实战项目【更换骨干网络为resnet】:橘子是否成熟检测(包含数据、代码、训练好的权重文件) (2000个子文件)
optimizer_config.json 2KB
README.zh-CN.md 41KB
README.md 41KB
README.md 11KB
README.md 11KB
CONTRIBUTING.md 5KB
README.md 2KB
dataloaders.py 59KB
general.py 50KB
export.py 41KB
train.py 34KB
val.py 24KB
torch_utils.py 21KB
__init__.py 21KB
val.py 20KB
plots.py 20KB
__init__.py 20KB
augmentations.py 18KB
predict.py 16KB
metrics.py 15KB
dataloaders.py 13KB
loss.py 11KB
clearml_utils.py 9KB
loss.py 9KB
hubconf.py 9KB
wandb_utils.py 8KB
autoanchor.py 7KB
hpo.py 7KB
plots.py 6KB
general.py 6KB
metrics.py 5KB
downloads.py 5KB
hpo.py 5KB
comet_utils.py 5KB
activations.py 5KB
triton.py 4KB
augmentations.py 4KB
__init__.py 3KB
autobatch.py 3KB
callbacks.py 3KB
restapi.py 2KB
resume.py 1KB
example_request.py 368B
__init__.py 0B
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__init__.py 0B
get_imagenet.sh 2KB
get_coco.sh 2KB
userdata.sh 1KB
mime.sh 780B
get_imagenet1000.sh 742B
get_imagenet100.sh 738B
get_imagenet10.sh 734B
download_weights.sh 641B
get_coco128.sh 619B
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