v1.0

e63cf68a · chenzk · e63cf68a · e63cf68a · e63cf68a · e63cf68a
Commit e63cf68a authored Jul 11, 2025 by chenzk
20 changed files
--- a/Arial.ttf
+++ b/Arial.ttf
--- a/LICENSE
+++ b/LICENSE
--- a/README.md
+++ b/README.md
+# YOLOv13
+YOLOv13引入超图理论，其中YOLOv13-N相比YOLO11-N提升了3.0%的mAP，相比YOLOv12-N提升了1.5%的mAP。
+## 论文
+`YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception`
+- https://arxiv.org/pdf/2506.17733
+
+## 模型结构
+遵循“骨干网络→颈部网络→检测头”的计算范式，通过超图自适应关联增强（HyperACE）机制，实现全链路特征聚合与分配（FullPAD），从而增强传统的YOLO架构。
+<div align=center>
+    <img src="./doc/YOLOv13.png"/>
+</div>
+
+## 算法原理
+YOLOv13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，最后经过head预测出检测框，其中YOLOv13最大的改进是引入超图的自适应关联增强机制HyperACE，将多尺度特征图的像素视为超图顶点，HyperACE 设计了可学习的超边生成模块，自适应地学习并构建超边，动态探索不同特征顶点间的潜在关联。
+<div align=center>
+    <img src="./doc/HyperACE.png"/>
+</div>
+
+## 环境配置
+```
+mv yolov13_pytorch yolov13 # 去框架名后缀
+```
+### 硬件需求
+DCU型号：K100AI，节点数量：1 台，卡数：4 张。
+
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04.1-py3.10
+# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：e50d644287fd
+docker run -it --shm-size=64G -v $PWD/yolov13:/home/yolov13 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name yolov13 <your IMAGE ID> bash
+cd /home/yolov13
+pip install -r requirements.txt # requirements.txt
+pip install -e . # ultralytics==8.3.63
+```
+### Dockerfile（方法二）
+```
+cd /home/yolov13/docker
+docker build --no-cache -t yolov13:latest .
+docker run --shm-size=64G --name yolov13 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/../../yolov13:/home/yolov13 -it yolov13 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。
+pip install -e . # ultralytics==8.3.63
+```
+### 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.sourcefind.cn/tool/
+```
+DTK驱动:25.04.1
+python:python3.10
+torch:2.4.1
+torchvision:0.19.1
+triton:3.0.0
+flash-attn:2.6.1
+deepspeed:0.14.2
+apex:1.4.0
+onnxruntime:1.19.2
+```
+
+`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
+```
+cd /home/yolov13
+pip install -r requirements.txt # requirements.txt
+pip install -e . # ultralytics==8.3.63
+```
+
+## 数据集
+[COCO2017](https://modelscope.cn/datasets/PAI/COCO2017)、[coco2017labels-segments](https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels-segments.zip)。
+
+数据集的构造方式：
+```
+# 1、创建datasets文件
+mkdir /home/datasets
+# 2、将coco2017labels-segments.zip放到/home/datasets/下面解压，生成数据集主体目录结构。
+unzip coco2017labels-segments.zip
+# 3、解压COCO2017中的train2017.zip和val2017.zip，并替换/home/datasets/coco/images/下相应的train2017和val2017即可。
+```
+完整的数据集目录结构如下：
+```
+/home/datasets/coco/
+    ├── train2017.txt
+    ├── val2017.txt
+    ├── test-dev2017.txt
+    ├── annotations/instances_val2017.json
+    ├── images
+        ├── train2017
+            ├── xxx.jpg
+            ...
+        └── val2017
+            ├── xxx.jpg
+            ...
+    └── labels
+        ├── train2017
+            ├── xxx.txt
+            ...
+        └── val2017
+            ├── xxx.txt
+            ...
+    ...
+```
+更多资料可参考源项目的[`README_origin`](./README_origin.md)
+
+## 训练
+
+运行训练命令时若未自动下载字体文件，使用以下命令放置即可：
+```
+cp Arial.ttf /root/.config/yolov13/Arial.ttf
+```
+
+### 单机多卡
+
+```
+cd /home/yolov13
+python train.py # 此处以yolov13n为例，其它参数量的模型以此类推。
+```
+
+## 推理
+
+### 单机多卡
+
+```
+cd /home/yolov13
+python prediction.py # 此处以yolov13n为例，其它参数量的模型以此类推。
+```
+更多资料可参考源项目的[`README_origin`](./README_origin.md)
+
+## result
+`输入: `
+```
+/home/yolov13/ultralytics/assets/bus.jpg
+/home/yolov13/ultralytics/assets/zidane.jpg
+```
+
+`输出:`
+```
+image 1/2 /home/yolov13/ultralytics/assets/bus.jpg: 640x480 5 persons, 1 bus
+image 2/2 /home/yolov13/ultralytics/assets/zidane.jpg: 384x640 2 persons, 1 tie
+```
+
+### 精度
+测试数据：`/home/datasets/coco/images/val2017`，测试权重为训练结束的`Validating /home/yolov13/runs/detect/train/weights/best.pt`，使用的加速卡：K100AI，max epoch为600，推理框架：pytorch。
+
+| device | mAP50 | mAP75 | mAP50-95 |
+| :------: | :------: | :------: | :------: |
+| DCU K100AI | 0.567 | 0.445 | 0.411 |
+| GPU A800 | 0.564 | 0.441 | 0.41 |
+
+## 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`
+## 预训练权重
+`weights/yolov13n.pt`
+## 源码仓库及问题反馈
+- http://developer.sourcefind.cn/codes/modelzoo/yolov13.git
+## 参考资料
+- https://github.com/iMoonLab/yolov13.git
+
--- a/README_origin.md
+++ b/README_origin.md
+<p align="center">
+    <img src="assets/icon.png" width="110" style="margin-bottom: 0.2;"/>
+<p>
+<h2 align="center">YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception</h2>
+
+<p align="center">
+    <a href="https://arxiv.org/abs/2506.17733">
+    <img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="arXiv">
+  </a>
+  <a href="https://github.com/iMoonLab">
+    <img src="https://img.shields.io/badge/iMoonLab-Homepage-blueviolet.svg" alt="iMoonLab">
+  </a>
+</p>
+
+  
+<div align="center">
+    <img src="assets/framework.png" width="96%" height="96%">
+</div>
+
+## Updates
+
+- 2025/06/27: [Converting YOLOv13](https://github.com/kaylorchen/ai_framework_demo) to Huawei Ascend (OM), Rockchip (RKNN) formats is supported. Thanks to [kaylorchen](https://github.com/kaylorchen)!
+
+- 2025/06/25: [FastAPI REST API](https://github.com/iMoonLab/yolov13/tree/main/examples/YOLOv13-FastAPI-REST-API) is supported. Thanks to [MohibShaikh](https://github.com/MohibShaikh)!
+
+- 2025/06/24: 🔥 **The paper of YOLOv13 can be downloaded**: [🔗 YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception](https://arxiv.org/abs/2506.17733).
+
+- 2025/06/24: [Android deployment](https://github.com/mpj1234/ncnn-yolov13-android/tree/main) is supported. Thanks to [mpj1234](https://github.com/mpj1234)!
+
+- 2025/06/22: YOLOv13 model weights released.
+
+- 2025/06/21: The code of YOLOv13 has been open-sourced.
+
+
+
+<h2>Table of Contents</h2>
+
+- [Technical Briefing 💡](#technical-briefing-)
+- [Main Results 🏆](#main-results-)
+  - [1. MS COCO Benchmark](#1-ms-coco-benchmark)
+  - [2. Visualizations](#2-visualizations)
+- [Quick Start 🚀](#quick-start-)
+  - [1. Install Dependencies](#1-install-dependencies)
+  - [2. Validation](#2-validation)
+  - [3. Training](#3-training)
+  - [4. Prediction](#4-prediction)
+  - [5. Export](#5-export)
+- [Related Projects 🔗](#related-projects-)
+- [Cite YOLOv13 📝](#cite-yolov13-)
+
+
+
+## Technical Briefing 💡
+
+
+**Introducing YOLOv13**—the next-generation real-time detector with cutting-edge performance and efficiency. YOLOv13 family includes four variants: Nano, Small, Large, and X-Large, powered by:
+
+* **HyperACE: Hypergraph-based Adaptive Correlation Enhancement**
+
+  * Treats pixels in multi-scale feature maps as hypergraph vertices.
+  * Adopts a learnable hyperedge construction module to adaptively exploring high-order correlations between vertices.
+  * A message passing module with linear complexity is leveraged to effectively aggregate multi-scale features with the guidance of high-order correlations to achieve effective visual perception of complex scenarios.
+
+* **FullPAD: Full-Pipeline Aggregation-and-Distribution Paradigm**
+
+  * Uses the HyperACE to aggregate multi-scale features of the backbone and extract high-order correlations in the hypergraph space.
+  * FullPAD paradigm further leverages three separate tunnels to forward these correlation-enhanced features to the connection between the backbone and neck, the internal layers of the neck, and the connection between the neck and head, respectively. In this way, YOLOv13 achieves fine‑grained information flow and representational synergy across the entire pipeline.
+  * FullPAD significantly improves gradient propagation and enhances the detection performance.
+
+* **Model Lightweighting via DS-based Blocks**
+
+  * Replaces large-kernel convolutions with blocks building based on depthwise separable convolutions (DSConv, DS-Bottleneck, DS-C3k, DS-C3k2), preserving receptive field while greatly reducing parameters and computation.
+  * Achieves faster inference speed without sacrificing accuracy.
+
+> YOLOv13 seamlessly combines hypergraph computation with end-to-end information collaboration to deliver a more accurate, robust, and efficient real-time detection solution.
+
+
+
+## Main Results 🏆
+
+### 1. MS COCO Benchmark
+
+**Table 1. Quantitative comparison with other state-of-the-art real-time object detectors on the MS COCO dataset**
+
+
+| **Method** | **FLOPs (G)** | **Parameters(M)** | **AP<sub>50:95</sub><sup>val</sup>** | **AP<sub>50</sub><sup>val</sup>** | **AP<sub>75</sub><sup>val</sup>** | **Latency (ms)** |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
+| YOLOv6-3.0-N | 11.4 | 4.7 | 37.0 | 52.7 | – | 2.74 |
+| Gold-YOLO-N | 12.1 | 5.6 | 39.6 | 55.7 | – | 2.97 |
+| YOLOv8-N | 8.7 | 3.2 | 37.4 | 52.6 | 40.5 | 1.77 |
+| YOLOv10-N | 6.7 | 2.3 | 38.5 | 53.8 | 41.7 | 1.84 |
+| YOLO11-N | 6.5 | 2.6 | 38.6 | 54.2 | 41.6 | 1.53 |
+| YOLOv12-N | 6.5 | 2.6 | 40.1 | 56.0 | 43.4 | 1.83 |
+| **YOLOv13-N** | **6.4** | **2.5** | **41.6** | **57.8** | **45.1** | **1.97** |
+|           |           |            |                 |           | 
+| YOLOv6-3.0-S | 45.3 | 18.5 | 44.3 | 61.2 | – | 3.42 |
+| Gold-YOLO-S | 46.0 | 21.5 | 45.4 | 62.5 | – | 3.82 |
+| YOLOv8-S | 28.6 | 11.2 | 45.0 | 61.8 | 48.7 | 2.33 |
+| RT-DETR-R18 | 60.0 | 20.0 | 46.5 | 63.8 | – | 4.58 |
+| RT-DETRv2-R18 | 60.0 | 20.0 | 47.9 | 64.9 | – | 4.58 |
+| YOLOv9-S | 26.4 | 7.1 | 46.8 | 63.4 | 50.7 | 3.44 |
+| YOLOv10-S | 21.6 | 7.2 | 46.3 | 63.0 | 50.4 | 2.53 |
+| YOLO11-S | 21.5 | 9.4 | 45.8 | 62.6 | 49.8 | 2.56 |
+| YOLOv12-S | 21.4 | 9.3 | 47.1 | 64.2 | 51.0 | 2.82 |
+| **YOLOv13-S** | **20.8** | **9.0** | **48.0** | **65.2** | **52.0** | **2.98** |
+|           |           |            |                 |           | 
+| YOLOv6-3.0-L | 150.7 | 59.6 | 51.8 | 69.2 | – | 9.01 |
+| Gold-YOLO-L | 151.7 | 75.1 | 51.8 | 68.9 | – | 10.69 |
+| YOLOv8-L | 165.2 | 43.7 | 53.0 | 69.8 | 57.7 | 8.13 |
+| RT-DETR-R50 | 136.0 | 42.0 | 53.1 | 71.3 | – | 6.93 |
+| RT-DETRv2-R50 | 136.0 | 42.0 | 53.4 | 71.6 | – | 6.93 |
+| YOLOv9-C | 102.1 | 25.3 | 53.0 | 70.2 | 57.8 | 6.64 |
+| YOLOv10-L | 120.3 | 24.4 | 53.2 | 70.1 | 57.2 | 7.31 |
+| YOLO11-L | 86.9 | 25.3 | 52.3 | 69.2 | 55.7 | 6.23 |
+| YOLOv12-L | 88.9 | 26.4 | 53.0 | 70.0 | 57.9 | 7.10 |
+| **YOLOv13-L** | **88.4** | **27.6** | **53.4** | **70.9** | **58.1** | **8.63** |
+|           |           |            |                 |           | 
+| YOLOv8-X | 257.8 | 68.2 | 54.0 | 71.0 | 58.8 | 12.83 |
+| RT-DETR-R101 | 259.0 | 76.0 | 54.3 | 72.7 | – | 13.51 |
+| RT-DETRv2-R101| 259.0 | 76.0 | 54.3 | 72.8 | – | 13.51 |
+| YOLOv10-X | 160.4 | 29.5 | 54.4 | 71.3 | 59.3 | 10.70 |
+| YOLO11-X | 194.9 | 56.9 | 54.2 | 71.0 | 59.1 | 11.35 |
+| YOLOv12-X | 199.0 | 59.1 | 54.4 | 71.1 | 59.3 | 12.46 |
+| **YOLOv13-X** | **199.2** | **64.0** | **54.8** | **72.0** | **59.8** | **14.67** |
+
+
+### 2. Visualizations
+
+<div>
+    <img src="assets/vis.png" width="100%" height="100%">
+</div>
+
+**Visualization examples of YOLOv10-N/S, YOLO11-N/S, YOLOv12-N/S, and YOLOv13-N/S.**
+
+<div>
+    <img src="assets/hyperedge.png" width="60%" height="60%">
+</div>
+
+**Representative visualization examples of adaptive hyperedges. The hyperedges in the first and second columns mainly focus on the high-order interactions among objects in the foreground. The third column mainly focuses on the high-order interactions between the background and part of the foreground. The visualization of these hyperedges can intuitively reflect the high-order visual associations modeled by the YOLOv13.**
+
+
+
+## Quick Start 🚀
+
+### 1. Install Dependencies
+
+```
+wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
+conda create -n yolov13 python=3.11
+conda activate yolov13
+pip install -r requirements.txt
+pip install -e .
+```
+YOLOv13 suppports Flash Attention acceleration.
+
+### 2. Validation
+[`YOLOv13-N`](https://github.com/iMoonLab/yolov13/releases/download/yolov13/yolov13n.pt)
+[`YOLOv13-S`](https://github.com/iMoonLab/yolov13/releases/download/yolov13/yolov13s.pt)
+[`YOLOv13-L`](https://github.com/iMoonLab/yolov13/releases/download/yolov13/yolov13l.pt)
+[`YOLOv13-X`](https://github.com/iMoonLab/yolov13/releases/download/yolov13/yolov13x.pt)
+
+Use the following code to validate the YOLOv13 models on the COCO dataset. Make sure to replace `{n/s/l/x}` with the desired model scale (nano, small, plus, or ultra).
+```python
+from ultralytics import YOLO
+
+model = YOLO('yolov13{n/s/l/x}.pt')  # Replace with the desired model scale
+```
+
+### 3. Training
+
+Use the following code to train the YOLOv13 models. Make sure to replace `yolov13n.yaml` with the desired model configuration file path, and `coco.yaml` with your coco dataset configuration file.
+```python
+from ultralytics import YOLO
+
+model = YOLO('yolov13n.yaml')
+
+# Train the model
+results = model.train(
+  data='coco.yaml',
+  epochs=600, 
+  batch=256, 
+  imgsz=640,
+  scale=0.5,  # S:0.9; L:0.9; X:0.9
+  mosaic=1.0,
+  mixup=0.0,  # S:0.05; L:0.15; X:0.2
+  copy_paste=0.1,  # S:0.15; L:0.5; X:0.6
+  device="0,1,2,3",
+)
+
+# Evaluate model performance on the validation set
+metrics = model.val('coco.yaml')
+
+# Perform object detection on an image
+results = model("path/to/your/image.jpg")
+results[0].show()
+
+```
+
+
+### 4. Prediction
+Use the following code to perform object detection using the YOLOv13 models. Make sure to replace `{n/s/l/x}` with the desired model scale.
+```python
+from ultralytics import YOLO
+
+model = YOLO('yolov13{n/s/l/x}.pt')  # Replace with the desired model scale
+model.predict()
+```
+
+### 5. Export
+Use the following code to export the YOLOv13 models to ONNX or TensorRT format. Make sure to replace `{n/s/l/x}` with the desired model scale.
+```python
+from ultralytics import YOLO
+model = YOLO('yolov13{n/s/l/x}.pt')  # Replace with the desired model scale
+model.export(format="engine", half=True)  # or format="onnx"
+```
+
+## Related Projects 🔗
+
+- The code is based on [Ultralytics](https://github.com/ultralytics/ultralytics). Thanks for their excellent work!
+- Other wonderful works about Hypergraph Computation:
+  - "Hypergraph Neural Networks": [[paper](https://arxiv.org/abs/1809.09401)] [[code](https://github.com/iMoonLab/HGNN)]
+  - "HGNN+: General Hypergraph Nerual Networks": [[paper](https://ieeexplore.ieee.org/abstract/document/9795251)] [[code](https://github.com/iMoonLab/DeepHypergraph)]
+  - "SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition": [[paper](https://arxiv.org/abs/2505.15325)] [[code](https://github.com/Mengqi-Lei/SoftHGNN)]
+
+## Cite YOLOv13 📝
+```bibtex
+@article{yolov13,
+  title={YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception},
+  author={Lei, Mengqi and Li, Siqi and Wu, Yihong and et al.},
+  journal={arXiv preprint arXiv:2506.17733},
+  year={2025}
+}
+```
+
--- a/assets/framework.png
+++ b/assets/framework.png
--- a/assets/hyperedge.png
+++ b/assets/hyperedge.png
--- a/assets/icon.png
+++ b/assets/icon.png
--- a/assets/vis.png
+++ b/assets/vis.png
--- a/doc/HyperACE.png
+++ b/doc/HyperACE.png
--- a/doc/YOLOv13.png
+++ b/doc/YOLOv13.png
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04.1-py3.10
+ENV DEBIAN_FRONTEND=noninteractive
+# RUN yum update && yum install -y git cmake wget build-essential
+# RUN source /opt/dtk25.04.1/env.sh
+# # 安装pip相关依赖
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
--- a/docker/requirements.txt
+++ b/docker/requirements.txt
+# torch==2.2.2 
+# torchvision==0.17.2
+# flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
+timm==1.0.14
+albumentations==2.0.4
+onnx==1.14.0
+# onnxruntime==1.15.1
+pycocotools==2.0.7
+PyYAML==6.0.1
+scipy # 1.13.0
+onnxslim==0.1.31
+# onnxruntime-gpu==1.18.0
+gradio==4.44.1
+opencv-python # 4.9.0.80
+psutil==5.9.8
+py-cpuinfo==9.0.0
+huggingface-hub==0.33.2
+safetensors==0.4.3
+numpy==1.26.4
+supervision==0.22.0
--- a/docker_nv/Dockerfile
+++ b/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 YOLO11 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:2.5.1-cuda12.4-cudnn9-runtime
+
+# Set environment variables
+# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1 \
+    MKL_THREADING_LAYER=GNU \
+    OMP_NUM_THREADS=1 
+
+# 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
+# libsm6 required by libqxcb to create QT-based windows for visualization; set 'QT_DEBUG_PLUGINS=1' to test in docker
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    gcc git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 libsm6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl tar
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Install pip packages
+RUN pip install uv
+# Note -cu12 must be used with tensorrt
+RUN uv pip install --system -e ".[export]" tensorrt-cu12 "albumentations>=1.4.6" comet pycocotools
+
+# Run exports to AutoInstall packages
+# Edge TPU export fails the first time so is run twice here
+RUN yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32 || yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32
+# Requires <= Python 3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+RUN uv pip install --system "paddlepaddle>=2.6.0" x2paddle
+# Fix error: `np.bool` was a deprecated alias for the builtin `bool` segmentation error in Tests
+RUN uv pip install --system numpy==1.23.5
+
+# Remove extra build files
+RUN rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
+
+# 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)"/shared/datasets:/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
--- a/docker_nv/Dockerfile-arm64
+++ b/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
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# 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
+# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    python3-pip git zip unzip wget curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Install pip packages
+RUN pip install uv
+RUN uv pip install --system -e ".[export]" --break-system-packages
+
+# Creates a symbolic link to make 'python' point to 'python3'
+RUN ln -sf /usr/bin/python3 /usr/bin/python
+
+# Remove extra build files
+RUN rm -rf /root/.config/Ultralytics/persistent_cache.json
+
+# 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)"/shared/datasets:/datasets $t
--- a/docker_nv/Dockerfile-conda
+++ b/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
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# 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-get update && \
+    apt-get install -y --no-install-recommends \
+    libgl1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy contents
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.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=12.1 -c pytorch -c nvidia && \
+    conda install -c conda-forge ultralytics mkl
+    # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=12.1 ultralytics mkl
+
+# Remove extra build files
+RUN rm -rf /root/.config/Ultralytics/persistent_cache.json
+
+# 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)"/shared/datasets:/datasets $t
--- a/docker_nv/Dockerfile-cpu
+++ b/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 YOLO11 deployments
+
+# Use official Python base image for reproducibility (3.11.10 for export and 3.12.6 for inference)
+FROM python:3.11.10-slim-bookworm
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# 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-get update && \
+    apt-get install -y --no-install-recommends \
+    python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Install pip packages
+RUN pip install uv
+RUN uv pip install --system -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-first-match
+
+# Run exports to AutoInstall packages
+RUN yolo export model=tmp/yolo11n.pt format=edgetpu imgsz=32
+RUN yolo export model=tmp/yolo11n.pt format=ncnn imgsz=32
+# Requires Python<=3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
+RUN uv pip install --system "paddlepaddle>=2.6.0" x2paddle
+
+# Remove extra build files
+RUN rm -rf tmp /root/.config/Ultralytics/persistent_cache.json
+
+# Set default command to bash
+CMD ["/bin/bash"]
+
+# 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)"/shared/datasets:/datasets $t
--- a/docker_nv/Dockerfile-jetson-jetpack4
+++ b/docker_nv/Dockerfile-jetson-jetpack4
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack4.x for YOLO11 on Jetson Nano, TX2, Xavier NX, AGX Xavier
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda
+FROM nvcr.io/nvidia/l4t-cuda:10.2.460-runtime
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# 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/
+
+# Add NVIDIA repositories for TensorRT dependencies
+RUN wget -q -O - https://repo.download.nvidia.com/jetson/jetson-ota-public.asc | apt-key add - && \
+  echo "deb https://repo.download.nvidia.com/jetson/common r32.7 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list && \
+  echo "deb https://repo.download.nvidia.com/jetson/t194 r32.7 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
+
+# Install dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create symbolic links for python3.8 and pip3
+RUN ln -sf /usr/bin/python3.8 /usr/bin/python3
+RUN ln -s /usr/bin/pip3 /usr/bin/pip
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Download onnxruntime-gpu 1.8.0 and tensorrt 8.2.0.6
+# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
+ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl
+ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl
+
+# Install pip packages
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install uv
+RUN uv pip install --system \
+    onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \
+    tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl
+RUN uv pip install --system -e ".[export]"
+
+# Remove extra build files
+RUN rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
--- a/docker_nv/Dockerfile-jetson-jetpack5
+++ b/docker_nv/Dockerfile-jetson-jetpack5
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson-jetson-jetpack5 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack5.1.2 for YOLO11 on Jetson Xavier NX, AGX Xavier, AGX Orin, Orin Nano and Orin NX
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack
+FROM nvcr.io/nvidia/l4t-jetpack:r35.4.1
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# 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 dependencies
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    git python3-pip libopenmpi-dev libopenblas-base libomp-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Pip install onnxruntime-gpu, torch, torchvision and ultralytics
+RUN python3 -m pip install --upgrade pip uv
+RUN uv pip install --system \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.18.0-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.1.0a0+41361538.nv23.06-cp38-cp38-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.16.2+c6f3977-cp38-cp38-linux_aarch64.whl
+
+RUN uv pip install --system -e ".[export]"
+
+# Remove extra build files
+RUN rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack5 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
--- a/docker_nv/Dockerfile-jetson-jetpack6
+++ b/docker_nv/Dockerfile-jetson-jetpack6
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:jetson-jetpack6 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Supports JetPack6.1 for YOLO11 on Jetson AGX Orin, Orin NX and Orin Nano Series
+
+# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack
+FROM nvcr.io/nvidia/l4t-jetpack:r36.4.0
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_BREAK_SYSTEM_PACKAGES=1
+
+# 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 dependencies
+ADD https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb .
+RUN dpkg -i cuda-keyring_1.1-1_all.deb && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+    git python3-pip libopenmpi-dev libopenblas-base libomp-dev libcusparselt0 libcusparselt-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create working directory
+WORKDIR /ultralytics
+
+# Copy contents and configure git
+COPY . .
+RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
+ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
+
+# Pip install onnxruntime-gpu, torch, torchvision and ultralytics
+RUN python3 -m pip install --upgrade pip uv
+RUN uv pip install --system \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.20.0-cp310-cp310-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.5.0a0+872d972e41.nv24.08-cp310-cp310-linux_aarch64.whl \
+    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.20.0a0+afc54f7-cp310-cp310-linux_aarch64.whl
+RUN uv pip install --system -e ".[export]"
+
+# Remove extra build files
+RUN rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack6 -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker run -it --ipc=host $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host $t
+
+# Pull and Run with NVIDIA runtime
+# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
--- a/docker_nv/Dockerfile-jupyter
+++ b/docker_nv/Dockerfile-jupyter
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Builds ultralytics/ultralytics:latest-jupyter image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
+# Image provides JupyterLab interface for interactive YOLO development and includes tutorial notebooks
+
+# Start from Python-based Ultralytics image for full Python environment
+FROM ultralytics/ultralytics:latest-python
+
+# Install JupyterLab for interactive development
+RUN uv pip install --system jupyterlab
+
+# Create persistent data directory structure
+RUN mkdir /data
+
+# Configure YOLO directories
+RUN mkdir /data/{datasets,weights,runs} && \
+    yolo settings datasets_dir="/data/datasets" weights_dir="/data/weights" runs_dir="/data/runs"
+
+# Start JupyterLab with tutorial notebook
+ENTRYPOINT ["/usr/local/bin/jupyter", "lab", "--allow-root", "--ip=*", "/ultralytics/examples/tutorial.ipynb"]
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker build -f docker/Dockerfile-jupyter -t $t . && sudo docker push $t
+
+# Run
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker run -it --ipc=host -p 8888:8888 $t
+
+# Pull and Run
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker pull $t && sudo docker run -it --ipc=host -p 8888:8888 $t
+
+# Pull and Run with local volume mounted
+# t=ultralytics/ultralytics:latest-jupyter && sudo docker pull $t && sudo docker run -it --ipc=host -p 8888:8888 -v "$(pwd)"/datasets:/data/datasets $t