description: Discover Ultralytics YOLOv8 - the latest in real-time object detection and image segmentation. Learn its features and maximize its potential in your projects.
description: Discover Ultralytics YOLO - the latest in real-time object detection and image segmentation. Learn its features and maximize its potential in your projects.
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Introducing [Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioner or new to the field, this hub aims to maximize YOLOv8's potential in your projects
Explore the Ultralytics Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioner or new to the field, this hub aims to maximize YOLO's potential in your projects
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@ -55,9 +55,9 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
## Where to Start
- **Install**`ultralytics` with pip and get up and running in minutes [:material-clock-fast: Get Started](quickstart.md){ .md-button }
- **Predict** new images and videos with YOLOv8 [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
- **Train** a new YOLOv8 model on your own custom dataset [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
- **Tasks** YOLOv8 tasks like segment, classify, pose and track [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
- **Predict** new images and videos with YOLO [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
- **Train** a new YOLO model on your own custom dataset [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
- **Tasks** YOLO tasks like segment, classify, pose and track [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
- **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest SOTA models [:material-magnify-expand: Explore a Dataset](models/yolo11.md){ .md-button }
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<strong>Watch:</strong> How to Train a YOLOv8 model on Your Custom Dataset in <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"target="_blank">Google Colab</a>.
<strong>Watch:</strong> How to Train a YOLO model on Your Custom Dataset in <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"target="_blank">Google Colab</a>.
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## YOLO: A Brief History
@ -99,7 +99,7 @@ Our licensing strategy is designed to ensure that any improvements to our open-s
### What is Ultralytics YOLO and how does it improve object detection?
Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. It builds on previous versions by introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports various [vision AI tasks](tasks/index.md) such as detection, segmentation, pose estimation, tracking, and classification. Its state-of-the-art architecture ensures superior speed and accuracy, making it suitable for diverse applications, including edge devices and cloud APIs.
Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. It builds on previous versions by introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLO supports various [vision AI tasks](tasks/index.md) such as detection, segmentation, pose estimation, tracking, and classification. Its state-of-the-art architecture ensures superior speed and accuracy, making it suitable for diverse applications, including edge devices and cloud APIs.
### How can I get started with YOLO installation and setup?
@ -122,7 +122,7 @@ Training a custom YOLO model on your dataset involves a few detailed steps:
For a detailed walkthrough, check out our [Train a Model](modes/train.md) guide, which includes examples and tips for optimizing your training process.
@ -141,7 +141,7 @@ For more details, visit our [Licensing](https://www.ultralytics.com/license) pag
Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
```bash
yolo track model=yolov8n.pt source=video.mp4
yolo track model=yolo11n.pt source=video.mp4
```
For a detailed guide on setting up and running object tracking, check our [tracking mode](modes/track.md) documentation, which explains the configuration and practical applications in real-time scenarios.
description: Learn how to install Ultralytics using pip, conda, or Docker. Follow our step-by-step guide for a seamless setup of YOLOv8 with thorough instructions.
description: Learn how to install Ultralytics using pip, conda, or Docker. Follow our step-by-step guide for a seamless setup of YOLO with thorough instructions.
Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLO via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
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@ -151,7 +151,7 @@ See the `ultralytics` [pyproject.toml](https://github.com/ultralytics/ultralytic
## Use Ultralytics with CLI
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLO from the command line.
!!! example
@ -172,28 +172,28 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
@ -212,18 +212,18 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
- `yolo predict model=yolo11n.pt, imgsz=640, conf=0.25` ❌ (do not use `,`)
- `yolo predict --model yolo11n.pt --imgsz 640 --conf 0.25` ❌ (do not use `--`)
[CLI Guide](usage/cli.md){ .md-button }
## Use Ultralytics with Python
YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in their projects. This makes YOLOv8's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
YOLO's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in their projects. This makes YOLO's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the [Python Guide](usage/python.md) to learn more about using YOLOv8 within your Python projects.
For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the [Python Guide](usage/python.md) to learn more about using YOLO within your Python projects.
!!! example
@ -231,10 +231,10 @@ For example, users can load a model, train it, evaluate its performance on a val
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO("yolov8n.yaml")
model = YOLO("yolo11n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
For more instructions, visit the [Conda quickstart guide](guides/conda-quickstart.md).
### What are the advantages of using Docker to run Ultralytics YOLOv8?
### What are the advantages of using Docker to run Ultralytics YOLO?
Using Docker to run Ultralytics YOLOv8 provides an isolated and consistent environment, ensuring smooth performance across different systems. It also eliminates the complexity of local installation. Official Docker images from Ultralytics are available on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), with different variants tailored for GPU, CPU, ARM64, NVIDIA Jetson, and Conda environments. Below are the commands to pull and run the latest image:
Using Docker to run Ultralytics YOLO provides an isolated and consistent environment, ensuring smooth performance across different systems. It also eliminates the complexity of local installation. Official Docker images from Ultralytics are available on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), with different variants tailored for GPU, CPU, ARM64, NVIDIA Jetson, and Conda environments. Below are the commands to pull and run the latest image:
```bash
# Pull the latest ultralytics image from Docker Hub
@ -410,9 +410,9 @@ pip install -e .
This approach allows you to contribute to the project or experiment with the latest source code. For more details, visit the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics).
### Why should I use Ultralytics YOLOv8 CLI?
### Why should I use Ultralytics YOLO CLI?
The Ultralytics YOLOv8 command line interface (CLI) simplifies running object detection tasks without requiring Python code. You can execute single-line commands for tasks like training, validation, and prediction straight from your terminal. The basic syntax for `yolo` commands is:
The Ultralytics YOLO command line interface (CLI) simplifies running object detection tasks without requiring Python code. You can execute single-line commands for tasks like training, validation, and prediction straight from your terminal. The basic syntax for `yolo` commands is:
```bash
yolo TASK MODE ARGS
@ -421,7 +421,7 @@ yolo TASK MODE ARGS
For example, to train a detection model with specified parameters:
Glenn Jocher
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