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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>
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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
```
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<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
基于YOLOV5 对水果图像数据集的目标检测实战项目,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 项目大小:73MB 项目迭代了100个epoch,最好的 MAP_0.5为0.97,MAP_0.5:0.95为0.73左右 关于yolov5训练脚本的参数介绍:https://blog.csdn.net/qq_44886601/article/details/136503688 关于yolov5推理脚本的参数介绍: https://blog.csdn.net/qq_44886601/article/details/136392838
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YOLOV5 实战项目:小型水果图像目标检测数据集 (853个子文件)
CITATION.cff 393B
results.csv 29KB
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
val_batch1_pred.jpg 498KB
apple_84.jpg 485KB
banana_85.jpg 484KB
val_batch1_labels.jpg 484KB
bus.jpg 476KB
apple_95.jpg 454KB
banana_89.jpg 454KB
orange_76.jpg 426KB
orange_34.jpg 423KB
train_batch0.jpg 419KB
train_batch1.jpg 404KB
orange_81.jpg 373KB
orange_60.jpg 371KB
banana_9.jpg 365KB
banana_13.jpg 362KB
val_batch0_pred.jpg 358KB
orange_81.jpg 357KB
banana_57.jpg 351KB
train_batch2.jpg 350KB
val_batch0_labels.jpg 344KB
banana_37.jpg 339KB
orange_76.jpg 338KB
apple_68.jpg 320KB
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mixed_12.jpg 316KB
apple_10.jpg 305KB
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banana_85.jpg 270KB
banana_2.jpg 265KB
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mixed_22.jpg 241KB
apple_88.jpg 239KB
orange_80.jpg 231KB
labels.jpg 228KB
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labels_correlogram.jpg 221KB
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mixed_5.jpg 196KB
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apple_65.jpg 192KB
mixed_14.jpg 191KB
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apple_95.jpg 189KB
banana_41.jpg 186KB
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orange_2.jpg 179KB
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zidane.jpg 165KB
banana_5.jpg 162KB
apple_7.jpg 161KB
apple_38.jpg 161KB
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apple_13.jpg 149KB
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banana_48.jpg 144KB
orange_41.jpg 140KB
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