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[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/) | [T眉rk莽e](https://docs.ultralytics.com/tr/) | [Ti岷縩g Vi峄噒](https://docs.ultralytics.com/vi/) | [啶灌た啶ㄠ啶︵](https://docs.ultralytics.com/hi/) | [丕賱毓乇亘賷丞](https://docs.ultralytics.com/ar/)
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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>
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<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
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YOLOv5改进 - 模块缝合 - C3 融合RFCAConv增强感受野空间特征 【二次融合 小白必备】 (161个子文件)
CITATION.cff 393B
Dockerfile 3KB
Dockerfile 821B
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
.dockerignore 4KB
.gitattributes 75B
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tutorial.ipynb 101KB
tutorial.ipynb 42KB
tutorial.ipynb 41KB
bus.jpg 476KB
zidane.jpg 165KB
optimizer_config.json 2KB
LICENSE 34KB
README.md 42KB
README.zh-CN.md 41KB
README.md 11KB
README.md 11KB
CONTRIBUTING.md 5KB
README.md 2KB
export.py 66KB
dataloaders.py 59KB
common.py 58KB
general.py 50KB
train.py 46KB
train.py 34KB
tf.py 31KB
val.py 30KB
val.py 24KB
hubconf.py 23KB
detect.py 23KB
__init__.py 21KB
torch_utils.py 21KB
yolo.py 20KB
plots.py 20KB
__init__.py 20KB
augmentations.py 18KB
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predict.py 16KB
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hpo.py 5KB
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experimental.py 5KB
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common.cpython-39.pyc 53KB
dataloaders.cpython-39.pyc 49KB
general.cpython-39.pyc 45KB
torch_utils.cpython-39.pyc 19KB
plots.cpython-39.pyc 19KB
augmentations.cpython-39.pyc 16KB
metrics.cpython-39.pyc 12KB
autoanchor.cpython-39.pyc 7KB
experimental.cpython-39.pyc 6KB
downloads.cpython-39.pyc 5KB
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__init__.cpython-39.pyc 131B
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
pyproject.toml 5KB
requirements.txt 2KB
additional_requirements.txt 264B
ImageNet1000.yaml 18KB
ImageNet.yaml 18KB
Objects365.yaml 9KB
xView.yaml 5KB
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