v1.0

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other info into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+mlruns/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Profiling
+*.pclprof
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+.idea
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# VSCode project settings
+.vscode/
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+mkdocs_github_authors.yaml
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# datasets and projects
+datasets/
+runs/
+wandb/
+tests/
+.DS_Store
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+weights/
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.mlpackage
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+*_paddle_model/
+pnnx*
+
+# Autogenerated files for tests
+/ultralytics/assets/

.pre-commit-config.yaml

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md
+# Optionally remove from local hooks with 'rm .git/hooks/pre-commit'
+
+# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions"
+  autoupdate_schedule: monthly
+  submodules: true
+
+# Exclude directories (optional)
+# exclude: 'docs/'
+
+# Define repos to run
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-case-conflict
+      # - id: check-yaml
+      - id: check-docstring-first
+      - id: detect-private-key
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.15.0
+    hooks:
+      - id: pyupgrade
+        name: Upgrade code
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.11
+    hooks:
+      - id: ruff
+        args: [--fix]
+
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.17
+    hooks:
+      - id: mdformat
+        name: MD formatting
+        additional_dependencies:
+          - mdformat-gfm
+          - mdformat-frontmatter
+          - mdformat-mkdocs
+        args:
+          - --wrap=no
+          - --number
+        exclude: 'docs/.*\.md'
+        # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6
+    hooks:
+      - id: codespell
+        exclude: "docs/de|docs/fr|docs/pt|docs/es|docs/mkdocs_de.yml"
+        args:
+          - --ignore-words-list=crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall
+
+  - repo: https://github.com/hadialqattan/pycln
+    rev: v2.4.0
+    hooks:
+      - id: pycln
+        args: [--all]
+#
+#  - repo: https://github.com/PyCQA/docformatter
+#    rev: v1.7.5
+#    hooks:
+#      - id: docformatter
+
+#  - repo: https://github.com/asottile/yesqa
+#    rev: v1.4.0
+#    hooks:
+#      - id: yesqa
+
+#  - repo: https://github.com/asottile/dead
+#    rev: v1.5.0
+#    hooks:
+#    -   id: dead
+
+#  - repo: https://github.com/ultralytics/pre-commit
+#    rev: bd60a414f80a53fb8f593d3bfed4701fc47e4b23
+#    hooks:
+#      - id: capitalize-comments

CONTRIBUTING.md

+# Contributing to YOLOv8 🚀
+
+We love your input! We want to make contributing to YOLOv8 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv8 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃!
+
+## Submitting a Pull Request (PR) 🛠️
+
+Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
+
+### 1. Select File to Update
+
+Select `requirements.txt` to update by clicking on it in GitHub.
+
+<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
+
+### 2. Click 'Edit this file'
+
+Button is in top-right corner.
+
+<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
+
+### 3. Make Changes
+
+Change `matplotlib` version from `3.2.2` to `3.3`.
+
+<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
+
+### 4. Preview Changes and Submit PR
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv8 for review and approval 😃!
+
+<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
+
+### PR recommendations
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+- ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
+
+<p align="center"><img width="751" alt="PR recommendation 1" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
+
+- ✅ Verify all YOLOv8 Continuous Integration (CI) **checks are passing**.
+
+<p align="center"><img width="751" alt="PR recommendation 2" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
+
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
+
+### Docstrings
+
+Not all functions or classes require docstrings but when they do, we follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
+
+```python
+"""
+    What the function does. Performs NMS on given detection predictions.
+
+    Args:
+        arg1: The description of the 1st argument
+        arg2: The description of the 2nd argument
+
+    Returns:
+        What the function returns. Empty if nothing is returned.
+
+    Raises:
+        Exception Class: When and why this exception can be raised by the function.
+"""
+```
+
+## Submitting a Bug Report 🐛
+
+If you spot a problem with YOLOv8 please submit a Bug Report!
+
+For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need in order to get started.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces the problem should be:
+
+- ✅ **Minimal** – Use as little code as possible that still produces the same problem
+- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be:
+
+- ✅ **Current** – Verify that your code is up-to-date with current GitHub [main](https://github.com/ultralytics/ultralytics/tree/main) branch, and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits.
+- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/ultralytics/issues/new/choose) and providing a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/)

README.md

+# YOLOv10
+提出了无NMS训练的一致双重分配，与YOLOv9-C相比，YOLOv10-B在相同性能下的延迟减少了46%，参数减少了25%。
+## 论文
+`YOLOv10: Real-Time End-to-End Object Detection`
+- https://arxiv.org/pdf/2405.14458
+
+## 模型结构
+YOLOv10通过Fork YOLOv8魔改而成，主体结构仍为YOLOv8，加入了一些修改的优化计算量的CIB、attention模块，同时将之前的one-to-many变为辅助one-to-one训练的正样本策略，使得在推理阶段可以去掉NMS来减小计算量。
+<div align=center>
+    <img src="./doc/structure.png"/>
+</div>
+
+## 算法原理
+yolov10将图片数据送入模型后，沿用yolo系列的通用方法，依次通过backbone、neck提取特征，然后经过head预测出检测框。
+<div align=center>
+    <img src="./doc/algorithm.png"/>
+</div>
+
+## 环境配置
+```
+mv yolov10_pytorch yolov10 # 去框架名后缀
+```
+
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：c85ed27005f2
+docker run -it --shm-size=32G -v $PWD/yolov10:/home/yolov10 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name yolov10 <your IMAGE ID> bash
+cd /home/yolov10
+pip install -r requirements.txt # requirements.txt
+```
+### Dockerfile（方法二）
+```
+cd yolov10/docker
+docker build --no-cache -t yolov10:latest .
+docker run --shm-size=32G --name yolov10 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../yolov10:/home/yolov10 -it yolov10 bash
+# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt。
+```
+### Anaconda（方法三）
+1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
+- https://developer.hpccube.com/tool/
+```
+DTK驱动:dtk24.04
+python:python3.10
+torch:2.1.0
+torchvision:0.16.0
+onnxruntime:1.15.0
+```
+
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
+
+2、其它非特殊库参照requirements.txt安装
+```
+pip install -r requirements.txt # requirements.txt
+```
+
+## 数据集
+`COCO2017`
+- http://cocodataset.org/#download
+
+项目中已提供用于试验训练的迷你数据集，使用[`coco128.zip`](./coco128.zip)即可，也可利用训练时的自动下载获取：
+```
+# 将coco128.zip放到../datasets/下后训练代码会自动解压并调用
+cp coco128.zip /home/datasets/
+```
+训练数据目录结构如下，用于正常训练的完整数据集请按此目录结构进行制备：
+```
+/home/datasets/coco
+            ├── annotations/
+            │   ├── instances_train2017.json
+            │   ├── instances_val2017.json
+            │   └── ...
+            ├── train2017/
+            │   ├── xxx.png
+            │   ├── xxx.png
+            │   └── ...
+            ├── val2017/
+            │   ├── xxx.png
+            │   ├── xxx.png
+            │   └── ...
+```
+
+## 训练
+### 单机多卡
+```
+python train.py #以yolov10s进行示例
+# 使用完整coco2017训练对train.py做以下修改即可：
+# model.train(data='coco128.yaml', model='yolov10s.yaml', epochs=500, batch=256, imgsz=640, device='0,1,2,3')
+model.train(data='coco.yaml', model='yolov10s.yaml', epochs=500, batch=256, imgsz=640, device='0,1,2,3')
+```
+更多资料可参考源项目的[`README_origin`](./README_origin.md)
+
+## 推理
+```
+python predict.py
+```
+
+## result
+`输入：`
+```
+/home/yolov10/ultralytics/assets/bus.jpg #见下图
+/home/yolov10/ultralytics/assets/zidane.jpg
+```
+<div align=center>
+    <img src="./doc/bus.png"/>
+</div>
+
+`输出：`
+```
+image 1/2 /home/yolov10/ultralytics/assets/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign
+image 2/2 /home/yolov10/ultralytics/assets/zidane.jpg: 384x640 2 persons, 1 tie
+```
+
+### 精度
+max epoch为500，推理框架：pytorch。
+
+|  device   |   mAP50-95   |
+|:---------:|:-------:|
+| DCU K100  | 0.451 |
+| GPU V100S | 0.451 |
+
+## 应用场景
+### 算法类别
+`目标检测`
+### 热点应用行业
+`制造,电商,医疗,能源,教育`
+## 源码仓库及问题反馈
+- http://developer.hpccube.com/codes/modelzoo/yolov10_pytorch.git
+## 参考资料
+- https://github.com/THU-MIG/yolov10.git
+

README_origin.md

+# [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458)
+
+
+Official PyTorch implementation of **YOLOv10**.
+
+<p align="center">
+  <img src="figures/latency.svg" width=48%>
+  <img src="figures/params.svg" width=48%> <br>
+  Comparisons with others in terms of latency-accuracy (left) and size-accuracy (right) trade-offs.
+</p>
+
+[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\
+Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
+[![arXiv](https://img.shields.io/badge/arXiv-2405.14458-b31b1b.svg)](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2)  [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/kadirnar/Yolov10)   [![Transformers.js Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Transformers.js-blue)](https://huggingface.co/spaces/Xenova/yolov10-web)
+
+<details>
+  <summary>
+  <font size="+1">Abstract</font>
+  </summary>
+Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance.
+</details>
+
+## Notes
+- 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
+- 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!
+- 2024/05/27: We have updated the [checkpoints](https://github.com/THU-MIG/yolov10/releases/tag/v1.1) with other attributes, like class names and training args, for ease of use.
+
+## UPDATES 🔥
+- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)
+- 2024/06/01: Thanks to [NielsRogge](https://github.com/NielsRogge) and [AK](https://x.com/_akhaliq) for hosting the models on the HuggingFace Hub!
+- 2024/05/31: Build [yolov10-jetson](https://github.com/Seeed-Projects/jetson-examples/blob/main/reComputer/scripts/yolov10/README.md) docker image by [youjiang](https://github.com/yuyoujiang)!
+- 2024/05/31: Thanks to [mohamedsamirx](https://github.com/mohamedsamirx) for the integration with [BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library](https://colab.research.google.com/drive/1-QV2TNfqaMsh14w5VxieEyanugVBG14V?usp=sharing)!
+- 2024/05/31: Thanks to [kaylorchen](https://github.com/kaylorchen) for the integration with [rk3588](https://github.com/kaylorchen/rk3588-yolo-demo)!
+- 2024/05/30: Thanks to [eaidova](https://github.com/eaidova) for the integration with [OpenVINO™](https://github.com/openvinotoolkit/openvino_notebooks/blob/0ba3c0211bcd49aa860369feddffdf7273a73c64/notebooks/yolov10-optimization/yolov10-optimization.ipynb)!
+- 2024/05/29: Add the gradio demo for running the models locally. Thanks to [AK](https://x.com/_akhaliq)!
+- 2024/05/27: Thanks to [sujanshresstha](sujanshresstha) for the integration with [DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT.git)!
+- 2024/05/26: Thanks to [CVHub520](https://github.com/CVHub520) for the integration into [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling)!
+- 2024/05/26: Thanks to [DanielSarmiento04](https://github.com/DanielSarmiento04) for integrate in [c++ | ONNX | OPENCV](https://github.com/DanielSarmiento04/yolov10cpp)!
+- 2024/05/25: Add [Transformers.js demo](https://huggingface.co/spaces/Xenova/yolov10-web) and onnx weights(yolov10[n](https://huggingface.co/onnx-community/yolov10n)/[s](https://huggingface.co/onnx-community/yolov10s)/[m](https://huggingface.co/onnx-community/yolov10m)/[b](https://huggingface.co/onnx-community/yolov10b)/[l](https://huggingface.co/onnx-community/yolov10l)/[x](https://huggingface.co/onnx-community/yolov10x)). Thanks to [xenova](https://github.com/xenova)!
+- 2024/05/25: Add [colab demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s), [HuggingFace Demo](https://huggingface.co/spaces/kadirnar/Yolov10), and [HuggingFace Model Page](https://huggingface.co/kadirnar/Yolov10). Thanks to [SkalskiP](https://github.com/SkalskiP) and [kadirnar](https://github.com/kadirnar)! 
+
+## Performance
+COCO
+
+| Model | Test Size | #Params | FLOPs | AP<sup>val</sup> | Latency |
+|:---------------|:----:|:---:|:--:|:--:|:--:|
+| [YOLOv10-N](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) |   640  |     2.3M    |   6.7G   |     38.5%     | 1.84ms |
+| [YOLOv10-S](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) |   640  |     7.2M    |   21.6G  |     46.3%     | 2.49ms |
+| [YOLOv10-M](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) |   640  |     15.4M   |   59.1G  |     51.1%     | 4.74ms |
+| [YOLOv10-B](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) |   640  |     19.1M   |  92.0G |     52.5%     | 5.74ms |
+| [YOLOv10-L](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) |   640  |     24.4M   |  120.3G   |     53.2%     | 7.28ms |
+| [YOLOv10-X](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt) |   640  |     29.5M    |   160.4G   |     54.4%     | 10.70ms |
+
+## Installation
+`conda` virtual environment is recommended. 
+```
+conda create -n yolov10 python=3.9
+conda activate yolov10
+pip install -r requirements.txt
+pip install -e .
+```
+## Demo
+```
+wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt
+python app.py
+# Please visit http://127.0.0.1:7860
+```
+
+## Validation
+[`yolov10n.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt)  [`yolov10s.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt)  [`yolov10m.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt)  [`yolov10b.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt)  [`yolov10l.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt)  [`yolov10x.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt)  
+```
+yolo val model=yolov10n/s/m/b/l/x.pt data=coco.yaml batch=256
+```
+
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# or
+model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.val(data='coco.yaml', batch=256)
+```
+
+
+## Training 
+```
+yolo detect train data=coco.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7
+```
+
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10()
+# If you want to finetune the model with pretrained weights, you could load the 
+# pretrained weights like below
+# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# Or
+# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)
+# Note that you can upload your trained model to HuggingFace Hub like below
+# model.push_to_hub("reponame", config={"model": "yolov10n/s/m/b/l/x.yaml"})
+```
+
+## Prediction
+Note that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to [here](https://github.com/THU-MIG/yolov10/issues/136) for details.
+```
+yolo predict model=yolov10n/s/m/b/l/x.pt
+```
+
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# or
+model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.predict()
+```
+
+## Export
+```
+# End-to-End ONNX
+yolo export model=yolov10n/s/m/b/l/x.pt format=onnx opset=13 simplify
+# Predict with ONNX
+yolo predict model=yolov10n/s/m/b/l/x.onnx
+
+# End-to-End TensorRT
+yolo export model=yolov10n/s/m/b/l/x.pt format=engine half=True simplify opset=13 workspace=16
+# Or
+trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
+# Predict with TensorRT
+yolo predict model=yolov10n/s/m/b/l/x.engine
+```
+
+Or
+```python
+from ultralytics import YOLOv10
+
+model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
+# or
+model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}.pt')
+
+model.export(...)
+```
+
+## Acknowledgement
+
+The code base is built with [ultralytics](https://github.com/ultralytics/ultralytics) and [RT-DETR](https://github.com/lyuwenyu/RT-DETR).
+
+Thanks for the great implementations! 
+
+## Citation
+
+If our code or models help your work, please cite our paper:
+```BibTeX
+@article{wang2024yolov10,
+  title={YOLOv10: Real-Time End-to-End Object Detection},
+  author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
+  journal={arXiv preprint arXiv:2405.14458},
+  year={2024}
+}
+```

app.py

+# Ackownledgement: https://huggingface.co/spaces/kadirnar/Yolov10/blob/main/app.py
+# Thanks to @kadirnar
+
+import gradio as gr
+from ultralytics import YOLOv10 
+
+def yolov10_inference(image, model_path, image_size, conf_threshold):
+    model = YOLOv10(model_path)
+    
+    model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True)
+    
+    return model.predictor.plotted_img[:, :, ::-1]
+
+def app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil", label="Image")
+                
+                model_id = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "yolov10n.pt",
+                        "yolov10s.pt",
+                        "yolov10m.pt",
+                        "yolov10b.pt",
+                        "yolov10l.pt",
+                        "yolov10x.pt",
+                    ],
+                    value="yolov10s.pt",
+                )
+                image_size = gr.Slider(
+                    label="Image Size",
+                    minimum=320,
+                    maximum=1280,
+                    step=32,
+                    value=640,
+                )
+                conf_threshold = gr.Slider(
+                    label="Confidence Threshold",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.25,
+                )
+                yolov10_infer = gr.Button(value="Detect Objects")
+
+            with gr.Column():
+                output_image = gr.Image(type="numpy", label="Annotated Image")
+
+        yolov10_infer.click(
+            fn=yolov10_inference,
+            inputs=[
+                image,
+                model_id,
+                image_size,
+                conf_threshold,
+            ],
+            outputs=[output_image],
+        )
+
+        gr.Examples(
+            examples=[
+                [
+                    "ultralytics/assets/bus.jpg",
+                    "yolov10s.pt",
+                    640,
+                    0.25,
+                ],
+                [
+                    "ultralytics/assets/zidane.jpg",
+                    "yolov10s.pt",
+                    640,
+                    0.25,
+                ],
+            ],
+            fn=yolov10_inference,
+            inputs=[
+                image,
+                model_id,
+                image_size,
+                conf_threshold,
+            ],
+            outputs=[output_image],
+            cache_examples=True,
+        )
+
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    YOLOv10: Real-Time End-to-End Object Detection
+    </h1>
+    """)
+    with gr.Row():
+        with gr.Column():
+            app()
+
+gradio_app.launch(debug=True)
\ No newline at end of file

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+ENV DEBIAN_FRONTEND=noninteractive
+# RUN yum update && yum install -y git cmake wget build-essential
+# RUN source /opt/dtk-24.04/env.sh
+# # 安装pip相关依赖
+COPY requirements.txt requirements.txt
+RUN pip3 install -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com -r requirements.txt
+

docker/requirements.txt

+# torch==2.0.1
+# torchvision==0.15.2
+onnx==1.14.0
+onnxruntime==1.15.1
+pycocotools==2.0.7
+PyYAML==6.0.1
+scipy==1.13.0
+onnxsim==0.4.36
+# onnxruntime-gpu==1.18.0
+gradio==4.31.5
+opencv-python==4.9.0.80
+psutil==5.9.8
+py-cpuinfo==9.0.0
+huggingface-hub==0.23.2
+safetensors==0.4.3

docker_nv/Dockerfile

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
+
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
+FROM pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime
+RUN pip install --no-cache nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl tar
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/ultralytics  # git permission issues inside container
+RUN git clone https://github.com/ultralytics/ultralytics -b main /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt /usr/src/ultralytics/
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" albumentations comet pycocotools
+
+# Run exports to AutoInstall packages
+# Edge TPU export fails the first time so is run twice here
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32 || yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+RUN pip install --no-cache paddlepaddle>=2.6.0 x2paddle
+# Fix error: `np.bool` was a deprecated alias for the builtin `bool` segmentation error in Tests
+RUN pip install --no-cache numpy==1.23.5
+# Remove exported models
+RUN rm -rf tmp
+
+# Set environment variables
+ENV OMP_NUM_THREADS=1
+# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377
+ENV MKL_THREADING_LAYER=GNU
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
+
+# Pull and Run with access to all GPUs
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
+
+# Pull and Run with access to GPUs 2 and 3 (inside container CUDA devices will appear as 0 and 1)
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus '"device=2,3"' $t
+
+# Pull and Run with local directory access
+# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
+
+# Kill all
+# sudo docker kill $(sudo docker ps -q)
+
+# Kill all image-based
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/ultralytics:latest)
+
+# DockerHub tag update
+# t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
+
+# Clean up
+# sudo docker system prune -a --volumes
+
+# Update Ubuntu drivers
+# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
+
+# DDP test
+# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
+
+# GCP VM from Image
+# docker.io/ultralytics/ultralytics:latest

docker_nv/Dockerfile-arm64

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is aarch64-compatible for Apple M1, M2, M3, Raspberry Pi and other ARM architectures
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu with "FROM arm64v8/ubuntu:22.04" (deprecated)
+# Start FROM Debian image for arm64v8 https://hub.docker.com/r/arm64v8/debian (new)
+FROM arm64v8/debian:bookworm-slim
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+# cmake and build-essential is needed to build onnxsim when exporting to tflite
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 build-essential
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/ultralytics  # git permission issues inside container
+RUN git clone https://github.com/ultralytics/ultralytics -b main /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt /usr/src/ultralytics/
+
+# Remove python3.11/EXTERNALLY-MANAGED to avoid 'externally-managed-environment' issue, Debian 12 Bookworm error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]"
+
+# Creates a symbolic link to make 'python' point to 'python3'
+RUN ln -sf /usr/bin/python3 /usr/bin/python
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

docker_nv/Dockerfile-conda

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage
+
+# Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3
+FROM continuumio/miniconda3:latest
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+RUN apt update \
+    && apt install --no-install-recommends -y libgl1
+
+# Copy contents
+ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt .
+
+# Install conda packages
+# mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory'
+RUN conda config --set solver libmamba && \
+    conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia && \
+    conda install -c conda-forge ultralytics mkl
+    # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics mkl
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

docker_nv/Dockerfile-cpu

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM ubuntu:23.10
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/ultralytics  # git permission issues inside container
+RUN git clone https://github.com/ultralytics/ultralytics -b main /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt /usr/src/ultralytics/
+
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+# RUN pip install --no-cache paddlepaddle>=2.6.0 x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+# Creates a symbolic link to make 'python' point to 'python3'
+RUN ln -sf /usr/bin/python3 /usr/bin/python
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host --name NAME $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host --name NAME $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

docker_nv/Dockerfile-jetson

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack for YOLOv8 on Jetson Nano, TX1/TX2, Xavier NX, AGX Xavier, AGX Orin, and Orin NX
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch
+FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/ultralytics  # git permission issues inside container
+RUN git clone https://github.com/ultralytics/ultralytics -b main /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt /usr/src/ultralytics/
+
+# Remove opencv-python from Ultralytics dependencies as it conflicts with opencv-python installed in base image
+RUN grep -v "opencv-python" pyproject.toml > temp.toml && mv temp.toml pyproject.toml
+
+# Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache tqdm matplotlib pyyaml psutil pandas onnx "numpy==1.23"
+RUN pip install --no-cache -e .
+
+# Set environment variables
+ENV OMP_NUM_THREADS=1
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t

docker_nv/Dockerfile-python

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
+
+# Use the official Python 3.10 slim-bookworm as base image
+FROM python:3.10-slim-bookworm
+
+# Downloads to user config dir
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
+    /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+
+# Create working directory
+WORKDIR /usr/src/ultralytics
+
+# Copy contents
+# COPY . /usr/src/ultralytics  # git permission issues inside container
+RUN git clone https://github.com/ultralytics/ultralytics -b main /usr/src/ultralytics
+ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt /usr/src/ultralytics/
+
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+# RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+RUN pip install --no-cache paddlepaddle>=2.6.0 x2paddle
+# Remove exported models
+RUN rm -rf tmp
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-python && sudo docker build -f docker/Dockerfile-python -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

docker_nv/Dockerfile-runner

+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference tests
+
+# Start FROM Ultralytics GPU image
+FROM ultralytics/ultralytics:latest
+
+# Set the working directory
+WORKDIR /actions-runner
+
+# Download and unpack the latest runner from https://github.com/actions/runner
+RUN FILENAME=actions-runner-linux-x64-2.309.0.tar.gz && \
+    curl -o $FILENAME -L https://github.com/actions/runner/releases/download/v2.309.0/$FILENAME && \
+    tar xzf $FILENAME && \
+    rm $FILENAME
+
+# Install runner dependencies
+ENV RUNNER_ALLOW_RUNASROOT=1
+ENV DEBIAN_FRONTEND=noninteractive
+RUN ./bin/installdependencies.sh && \
+    apt-get -y install libicu-dev
+
+# Inline ENTRYPOINT command to configure and start runner with default TOKEN and NAME
+ENTRYPOINT sh -c './config.sh --url https://github.com/ultralytics/ultralytics \
+                              --token ${GITHUB_RUNNER_TOKEN:-TOKEN} \
+                              --name ${GITHUB_RUNNER_NAME:-NAME} \
+                              --labels gpu-latest \
+                              --replace && \
+                  ./run.sh'
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t
+
+# Pull and Run in detached mode with access to GPUs 0 and 1
+# t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t