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<p>
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</p>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) <br>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
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<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
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</div>
<br>
[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/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).
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" alt="YOLOv8 performance plots"></a>
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<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%" alt="space">
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<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>
## <div align="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
<details open>
<summary>Install</summary>
Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
[![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
```
For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart).
</details>
<details open>
<summary>Usage</summary>
#### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
```
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
#### Python
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
```
See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
</details>
## <div align="center">Models</div>
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/c
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YOLOv8 图像分类项目与 MNIST160 手写数字图片数据集集成
共1036个文件
md:522个
png:160个
py:138个
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2023-12-23
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描述: 这个项目展示了如何将 MNIST160 手写数字图片数据集成功集成到 YOLOv8 图像分类框架中。通过此集成,项目成功地运用了 YOLOv8 的先进算法对手写数字进行快速、准确的识别和分类。MNIST160 数据集,包含160张高质量的手写数字图片,被优化并用于这个先进的图像分类任务,展示了 YOLOv8 在处理实际应用场景中的强大能力。 总结: 整合 MNIST160 数据集与 YOLOv8 的这个项目不仅展示了如何有效地运用最新的图像分类技术,也提供了一个实用的案例,用于探索和优化机器学习在实际应用中的潜能。
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YOLOv8 图像分类项目与 MNIST160 手写数字图片数据集集成 (1036个子文件)
train.cache 8KB
test.cache 8KB
main.cc 10KB
CITATION.cff 612B
setup.cfg 2KB
CNAME 21B
inference.cpp 13KB
inference.cpp 6KB
main.cpp 5KB
main.cpp 2KB
style.css 1KB
Dockerfile 4KB
Dockerfile-arm64 2KB
Dockerfile-conda 2KB
Dockerfile-cpu 2KB
Dockerfile-jetson 2KB
Dockerfile-python 2KB
Dockerfile-runner 2KB
.gitignore 2KB
inference.h 2KB
inference.h 2KB
comments.html 2KB
source-file.html 858B
favicon.ico 9KB
MANIFEST.in 200B
tutorial.ipynb 33KB
hub.ipynb 4KB
bus.jpg 134KB
zidane.jpg 49KB
extra.js 3KB
LICENSE 34KB
predict.md 40KB
train.md 38KB
predict.md 36KB
quickstart.md 34KB
track.md 31KB
README.md 29KB
README.zh-CN.md 28KB
train.md 25KB
sam.md 25KB
yolov8.md 24KB
cfg.md 23KB
predict.md 23KB
quickstart.md 23KB
model-deployment-options.md 23KB
yolov8.md 23KB
sam.md 22KB
yolov8.md 21KB
track.md 21KB
openvino.md 20KB
yolov8.md 19KB
yolov8.md 19KB
yolov8.md 19KB
yolov8.md 19KB
index.md 19KB
fast-sam.md 19KB
yolov8.md 19KB
predict.md 19KB
quickstart.md 19KB
yolov8.md 18KB
yolov8.md 18KB
train.md 18KB
pose.md 18KB
sam.md 17KB
yolov8.md 17KB
yolo-common-issues.md 17KB
train_custom_data.md 17KB
segment.md 17KB
quickstart.md 17KB
classify.md 17KB
track.md 16KB
detect.md 16KB
train.md 16KB
roboflow.md 16KB
index.md 16KB
yolov5.md 16KB
track.md 16KB
sam.md 16KB
segment.md 15KB
predict.md 15KB
fast-sam.md 15KB
train.md 15KB
sam.md 15KB
model_export.md 15KB
pose.md 15KB
index.md 15KB
isolating-segmentation-objects.md 15KB
sam.md 15KB
yolov5.md 15KB
detect.md 15KB
classify.md 14KB
sam.md 14KB
predict.md 14KB
inference_api.md 14KB
pytorch_hub_model_loading.md 14KB
heatmaps.md 14KB
predict.md 14KB
quickstart.md 14KB
segment.md 14KB
predict.md 14KB
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