Quickstart

Install Ultralytics

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 for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.

Watch: Ultralytics YOLO Quick Start Guide

Install

Pip install (recommended)Conda installGit cloneDocker

Install the ultralytics package using pip, or update an existing installation by running pip install -U ultralytics. Visit the Python Package Index (PyPI) for more details on the ultralytics package: https://pypi.org/project/ultralytics/.

# Install the ultralytics package from PyPI
pip install ultralytics

You can also install the ultralytics package directly from the GitHub repository. This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @my-branch, or removed entirely to default to main branch.

# Install the ultralytics package from GitHub
pip install git+https://github.com/ultralytics/ultralytics.git@main

Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at https://anaconda.org/conda-forge/ultralytics. Ultralytics feedstock repository for updating the conda package is at https://github.com/conda-forge/ultralytics-feedstock/.

# Install the ultralytics package using conda
conda install -c conda-forge ultralytics

Note

If you are installing in a CUDA environment best practice is to install ultralytics, pytorch and pytorch-cuda in the same command to allow the conda package manager to resolve any conflicts, or else to install pytorch-cuda last to allow it override the CPU-specific pytorch package if necessary.

# Install all packages together using conda
conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics

Conda Docker Image

Ultralytics Conda Docker images are also available from DockerHub. These images are based on Miniconda3 and are an simple way to start using ultralytics in a Conda environment.

# Set image name as a variable
t=ultralytics/ultralytics:latest-conda

# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t

# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t  # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t  # specify GPUs

Clone the ultralytics repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode -e using pip.

# Clone the ultralytics repository
git clone https://github.com/ultralytics/ultralytics

# Navigate to the cloned directory
cd ultralytics

# Install the package in editable mode for development
pip install -e .

Utilize Docker to effortlessly execute the ultralytics package in an isolated container, ensuring consistent and smooth performance across various environments. By choosing one of the official ultralytics images from Docker Hub, you not only avoid the complexity of local installation but also benefit from access to a verified working environment. Ultralytics offers 5 main supported Docker images, each designed to provide high compatibility and efficiency for different platforms and use cases:

Dockerfile: GPU image recommended for training.
Dockerfile-arm64: Optimized for ARM64 architecture, allowing deployment on devices like Raspberry Pi and other ARM64-based platforms.
Dockerfile-cpu: Ubuntu-based CPU-only version suitable for inference and environments without GPUs.
Dockerfile-jetson: Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.
Dockerfile-python: Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.
Dockerfile-conda: Based on Miniconda3 with conda installation of ultralytics package.

Below are the commands to get the latest image and execute it:

# Set image name as a variable
t=ultralytics/ultralytics:latest

# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t

# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t  # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t  # specify GPUs

The above command initializes a Docker container with the latest ultralytics image. The -it flag assigns a pseudo-TTY and maintains stdin open, enabling you to interact with the container. The --ipc=host flag sets the IPC (Inter-Process Communication) namespace to the host, which is essential for sharing memory between processes. The --gpus all flag enables access to all available GPUs inside the container, which is crucial for tasks that require GPU computation.

Note: To work with files on your local machine within the container, use Docker volumes for mounting a local directory into the container:

# Mount local directory to a directory inside the container
sudo docker run -it --ipc=host --gpus all -v /path/on/host:/path/in/container $t

Alter /path/on/host with the directory path on your local machine, and /path/in/container with the desired path inside the Docker container for accessibility.

For advanced Docker usage, feel free to explore the Ultralytics Docker Guide.

See the ultralytics pyproject.toml file for a list of dependencies. Note that all examples above install all required dependencies.

Tip

PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at https://pytorch.org/get-started/locally.

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 to learn more about using YOLO from the command line.

Example

SyntaxTrainPredictValExportSpecial

Ultralytics yolo commands use the following syntax:

yolo TASK MODE ARGS

TASK (optional) is one of (detect, segment, classify, pose, obb)
MODE (required) is one of (train, val, predict, export, track, benchmark)
ARGS (optional) are arg=value pairs like imgsz=640 that override defaults.

See all ARGS in the full Configuration Guide or with the yolo cfg CLI command.

Train a detection model for 10 epochs with an initial learning_rate of 0.01

yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

Predict a YouTube video using a pretrained segmentation model at image size 320:

yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320

Val a pretrained detection model at batch-size 1 and image size 640:

yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640

Export a yolo11n classification model to ONNX format at image size 224 by 128 (no TASK required)

yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128

Run special commands to see version, view settings, run checks and more:

yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg

Warning

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 ✅
yolo predict model yolo11n.pt imgsz 640 conf 0.25 ❌ (missing =)
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

Use Ultralytics with Python

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, 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 to learn more about using YOLO within your Python projects.

Example

from ultralytics import YOLO

# Create a new YOLO model from scratch
model = YOLO("yolo11n.yaml")

# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolo11n.pt")

# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data="coco8.yaml", epochs=3)

# Evaluate the model's performance on the validation set
results = model.val()

# Perform object detection on an image using the model
results = model("https://ultralytics.com/images/bus.jpg")

# Export the model to ONNX format
success = model.export(format="onnx")

Python Guide

Ultralytics Settings

The Ultralytics library provides a powerful settings management system to enable fine-grained control over your experiments. By making use of the SettingsManager housed within the ultralytics.utils module, users can readily access and alter their settings. These are stored in a JSON file in the environment user configuration directory, and can be viewed or modified directly within the Python environment or via the Command-Line Interface (CLI).

Inspecting Settings

To gain insight into the current configuration of your settings, you can view them directly:

View settings

PythonCLI

You can use Python to view your settings. Start by importing the settings object from the ultralytics module. Print and return settings using the following commands:

from ultralytics import settings

# View all settings
print(settings)

# Return a specific setting
value = settings["runs_dir"]

Alternatively, the command-line interface allows you to check your settings with a simple command:

yolo settings

Modifying Settings

Ultralytics allows users to easily modify their settings. Changes can be performed in the following ways:

Update settings

PythonCLI

Within the Python environment, call the update method on the settings object to change your settings:

from ultralytics import settings

# Update a setting
settings.update({"runs_dir": "/path/to/runs"})

# Update multiple settings
settings.update({"runs_dir": "/path/to/runs", "tensorboard": False})

# Reset settings to default values
settings.reset()

If you prefer using the command-line interface, the following commands will allow you to modify your settings:

# Update a setting
yolo settings runs_dir='/path/to/runs'

# Update multiple settings
yolo settings runs_dir='/path/to/runs' tensorboard=False

# Reset settings to default values
yolo settings reset

Understanding Settings

The table below provides an overview of the settings available for adjustment within Ultralytics. Each setting is outlined along with an example value, the data type, and a brief description.

Name	Example Value	Data Type	Description
`settings_version`	`'0.0.4'`	`str`	Ultralytics settings version (different from Ultralytics pip version)
`datasets_dir`	`'/path/to/datasets'`	`str`	The directory where the datasets are stored
`weights_dir`	`'/path/to/weights'`	`str`	The directory where the model weights are stored
`runs_dir`	`'/path/to/runs'`	`str`	The directory where the experiment runs are stored
`uuid`	`'a1b2c3d4'`	`str`	The unique identifier for the current settings
`sync`	`True`	`bool`	Whether to sync analytics and crashes to HUB
`api_key`	`''`	`str`	Ultralytics HUB API Key
`clearml`	`True`	`bool`	Whether to use ClearML logging
`comet`	`True`	`bool`	Whether to use Comet ML for experiment tracking and visualization
`dvc`	`True`	`bool`	Whether to use DVC for experiment tracking and version control
`hub`	`True`	`bool`	Whether to use Ultralytics HUB integration
`mlflow`	`True`	`bool`	Whether to use MLFlow for experiment tracking
`neptune`	`True`	`bool`	Whether to use Neptune for experiment tracking
`raytune`	`True`	`bool`	Whether to use Ray Tune for hyperparameter tuning
`tensorboard`	`True`	`bool`	Whether to use TensorBoard for visualization
`wandb`	`True`	`bool`	Whether to use Weights & Biases logging
`vscode_msg`	`True`	`bool`	When VS Code terminal detected, enables prompt to download Ultralytics-Snippets extension.

As you navigate through your projects or experiments, be sure to revisit these settings to ensure that they are optimally configured for your needs.

FAQ

How do I install Ultralytics using pip?

To install Ultralytics with pip, execute the following command:

pip install ultralytics

For the latest stable release, this will install the ultralytics package directly from the Python Package Index (PyPI). For more details, visit the ultralytics package on PyPI.

Alternatively, you can install the latest development version directly from GitHub:

pip install git+https://github.com/ultralytics/ultralytics.git

Make sure to have the Git command-line tool installed on your system.

Can I install Ultralytics YOLO using conda?

Yes, you can install Ultralytics YOLO using conda by running:

conda install -c conda-forge ultralytics

This method is an excellent alternative to pip and ensures compatibility with other packages in your environment. For CUDA environments, it's best to install ultralytics, pytorch, and pytorch-cuda simultaneously to resolve any conflicts:

conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics

For more instructions, visit the Conda quickstart guide.

What are the advantages of using Docker to run Ultralytics YOLO?

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, with different variants tailored for GPU, CPU, ARM64, NVIDIA Jetson, and Conda environments. Below are the commands to pull and run the latest image:

# Pull the latest ultralytics image from Docker Hub
sudo docker pull ultralytics/ultralytics:latest

# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest

For more detailed Docker instructions, check out the Docker quickstart guide.

How do I clone the Ultralytics repository for development?

To clone the Ultralytics repository and set up a development environment, use the following steps:

# Clone the ultralytics repository
git clone https://github.com/ultralytics/ultralytics

# Navigate to the cloned directory
cd ultralytics

# Install the package in editable mode for development
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.

Why should I use Ultralytics YOLO CLI?

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:

yolo TASK MODE ARGS

For example, to train a detection model with specified parameters:

yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01

Check out the full CLI Guide to explore more commands and usage examples.

📅 Created 1 year ago ✏️ Updated 1 month ago