Quickstart (original) (raw)

Install Ultralytics

Ultralytics offers a variety of installation methods, including pip, conda, and Docker. You can install YOLO via the ultralytics pip package for the latest stable release, or by cloning the Ultralytics GitHub repository for the most current version. Docker is also an option to run the package in an isolated container, which avoids local installation.

Watch: Ultralytics YOLO Quick Start Guide

Install

Pip install (recommended)Conda installGit cloneDocker

Install or update the ultralytics package using pip by running pip install -U ultralytics. For more details on the ultralytics package, visit the Python Package Index (PyPI).

# Install the ultralytics package from PyPI pip install ultralytics

You can also install ultralytics directly from the Ultralytics GitHub repository. This can be useful if you want the latest development version. Ensure you have the Git command-line tool installed, and then run:

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

Conda can be used as an alternative package manager to pip. For more details, visit Anaconda. The Ultralytics feedstock repository for updating the conda package is available at GitHub.

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

Note

If you are installing in a CUDA environment, it is best practice to install ultralytics, pytorch, and pytorch-cuda in the same command. This allows the conda package manager to resolve any conflicts. Alternatively, install pytorch-cuda last to 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 provide a straightforward 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 GitHub repository if you are interested in contributing to 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 . `

Use Docker to execute the ultralytics package in an isolated container, ensuring consistent performance across various environments. By selecting one of the official ultralytics images from Docker Hub, you avoid the complexity of local installation and gain access to a verified working environment. Ultralytics offers five main supported Docker images, each designed for high compatibility and efficiency:

• Dockerfile: GPU image recommended for training.
• Dockerfile-arm64: Optimized for ARM64 architecture, suitable for 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 a conda installation of the ultralytics package.

Here 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 flags assign a pseudo-TTY and keep stdin open, allowing interaction 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, crucial for tasks requiring GPU computation.

Note: To work with files on your local machine within the container, use Docker volumes to mount 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

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

For advanced Docker usage, 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 install PyTorch first by following the instructions at PyTorch.

Custom Installation Methods

While the standard installation methods cover most use cases, you might need a more tailored setup. This could involve installing specific package versions, omitting optional dependencies, or substituting packages like replacing opencv-python with the GUI-less opencv-python-headless for server environments.

Custom Methods

Method 1: Install without dependencies (--no-deps)Method 2: Install from a Custom ForkMethod 3: Local Clone, Modify, and InstallMethod 4: Use requirements.txt

You can install the ultralytics package core without any dependencies using pip's --no-deps flag. This requires you to manually install all necessary dependencies afterward.

1. Install ultralytics core:
   pip install ultralytics --no-deps
2. Manually install dependencies: You need to install all required packages listed in the pyproject.toml file, substituting or modifying versions as needed. For the headless OpenCV example:
   `# Install other core dependencies
   pip install torch torchvision numpy matplotlib pandas pyyaml pillow psutil requests tqdm scipy seaborn ultralytics-thop

Install headless OpenCV instead of the default

pip install opencv-python-headless
`

Dependency Management

This method gives full control but requires careful management of dependencies. Ensure all required packages are installed with compatible versions by referencing the ultralytics pyproject.toml file.

If you need persistent custom modifications (like always using opencv-python-headless), you can fork the Ultralytics repository, make changes to pyproject.toml or other code, and install from your fork.

1. Fork the Ultralytics GitHub repository to your own GitHub account.
2. Clone your fork locally:
   git clone https://github.com/YOUR_USERNAME/ultralytics.git cd ultralytics
3. Create a new branch for your changes:
   git checkout -b custom-opencv
4. Modify pyproject.toml: Open pyproject.toml in a text editor and replace the line containing "opencv-python>=4.6.0" with "opencv-python-headless>=4.6.0" (adjust version as needed).
5. Commit and push your changes:
   git add pyproject.toml git commit -m "Switch to opencv-python-headless" git push origin custom-opencv
6. Install using pip with the git+https syntax, pointing to your branch:
   pip install git+https://github.com/YOUR_USERNAME/ultralytics.git@custom-opencv

This method ensures that your custom dependency set is used whenever you install from this specific URL. See Method 4 for using this in a requirements.txt file.

Similar to the standard "Git Clone" method for development, you can clone the repository locally, modify dependency files before installation, and then install in editable mode.

1. Clone the Ultralytics repository:
   git clone https://github.com/ultralytics/ultralytics cd ultralytics
2. Modify pyproject.toml: Edit the file to make your desired changes. For example, use sed (on Linux/macOS) or a text editor to replace opencv-python with opencv-python-headless.Using sed (verify the exact line in pyproject.toml first):
   `# Example: Replace the line starting with "opencv-python..."

Adapt the pattern carefully based on the current file content

sed -i'' -e 's/^\s*"opencv-python>=.*",/"opencv-python-headless>=4.8.0",/' pyproject.toml
_Or manually editpyproject.toml_ to change "opencv-python>=...to"opencv-python-headless>=...". 3. **Install** the package in editable mode (-e). Pip will now use your modified pyproject.toml` to resolve and install dependencies:

This approach is useful for testing local changes to dependencies or build configurations before committing them or for setting up specific development environments.

If you manage your project dependencies using a requirements.txt file, you can specify your custom Ultralytics fork directly within it. This ensures that anyone setting up the project gets your specific version with its modified dependencies (like opencv-python-headless).

1. Create or edit requirements.txt: Add a line pointing to your custom fork and branch (as prepared in Method 2).
   requirements.txt
   `# Core dependencies
   numpy
   matplotlib
   pandas
   pyyaml
   Pillow
   psutil
   requests>=2.23.0
   tqdm
   torch>=1.8.0 # Or specific version/variant
   torchvision>=0.9.0 # Or specific version/variant

Install ultralytics from a specific git commit or branch

Replace YOUR_USERNAME and custom-branch with your details

git+https://github.com/YOUR_USERNAME/ultralytics.git@custom-branch

Other project dependencies

flask

... etc

_Note: You don't need to list dependencies already required by your customultralyticsfork (likeopencv-python-headless) here, as pip will install them based on the fork's pyproject.toml._ 2. **Install** dependencies from the file: pip install -r requirements.txt
`

This method integrates seamlessly with standard Python project dependency management workflows while allowing you to pin ultralytics to your customized Git source.

Use Ultralytics with CLI

The Ultralytics command-line interface (CLI) allows for simple single-line commands without needing a Python environment. CLI requires no customization or Python code; run all tasks from the terminal with the yolo command. For more on using YOLO from the command line, see the CLI Guide.

Example

SyntaxTrainPredictValExportCountWorkoutQueueInference with StreamlitSpecial

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

Validate a pretrained detection model with a batch size of 1 and image size of 640:

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

Export a YOLOv11n classification model to ONNX format with an image size of 224x128 (no TASK required):

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

Count objects in a video or live stream using YOLO11:

`yolo solutions count show=True

yolo solutions count source="path/to/video.mp4" # specify video file path `

Monitor workout exercises using a YOLO11 pose model:

`yolo solutions workout show=True

yolo solutions workout source="path/to/video.mp4" # specify video file path

Use keypoints for ab-workouts

yolo solutions workout kpts="[5, 11, 13]" # left side yolo solutions workout kpts="[6, 12, 14]" # right side `

Use YOLO11 to count objects in a designated queue or region:

`yolo solutions queue show=True

yolo solutions queue source="path/to/video.mp4" # specify video file path

yolo solutions queue region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]" # configure queue coordinates `

Perform object detection, instance segmentation, or pose estimation in a web browser using Streamlit:

`yolo solutions inference

yolo solutions inference model="path/to/model.pt" # use model fine-tuned with Ultralytics Python package `

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

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

Warning

Arguments must be passed as arg=value pairs, split by an equals = sign and delimited by spaces. 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 --)
• yolo solution model=yolo11n.pt imgsz=640 conf=0.25 ❌ (use solutions, not solution)

CLI Guide

Use Ultralytics with Python

The Ultralytics YOLO Python interface offers seamless integration into Python projects, making it easy to load, run, and process model outputs. Designed for simplicity, the Python interface allows users to quickly implement object detection, segmentation, and classification. This makes the YOLO Python interface an invaluable tool for incorporating these functionalities into Python projects.

For instance, users can load a model, train it, evaluate its performance, and export it to ONNX format with just a few lines of code. Explore 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 includes a SettingsManager for fine-grained control over experiments, allowing users to access and modify settings easily. Stored in a JSON file within the environment's user configuration directory, these settings can be viewed or modified in the Python environment or via the Command-Line Interface (CLI).

Inspecting Settings

To view the current configuration of your settings:

View settings

PythonCLI

Use Python to view your settings by importing the settings object from the ultralytics module. Print and return settings with these commands:

`from ultralytics import settings

View all settings

print(settings)

Return a specific setting

value = settings["runs_dir"] `

The command-line interface allows you to check your settings with:

Modifying Settings

Ultralytics makes it easy to modify settings in the following ways:

Update settings

PythonCLI

In Python, use the update method on the settings object:

`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() `

To modify settings using the command-line interface:

`# 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 overviews the adjustable settings within Ultralytics, including example values, data types, and descriptions.

Revisit these settings as you progress through projects or experiments to ensure optimal configuration.

FAQ

How do I install Ultralytics using pip?

Install Ultralytics with pip using:

This installs the latest stable release of the ultralytics package from PyPI. To install the development version directly from GitHub:

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

Ensure the Git command-line tool is installed on your system.

Can I install Ultralytics YOLO using conda?

Yes, install Ultralytics YOLO using conda with:

conda install -c conda-forge ultralytics

This method is a great alternative to pip, ensuring compatibility with other packages. For CUDA environments, install ultralytics, pytorch, and pytorch-cuda together to resolve conflicts:

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

For more instructions, see the Conda quickstart guide.

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

Docker provides an isolated, consistent environment for Ultralytics YOLO, ensuring smooth performance across systems and avoiding local installation complexities. Official Docker images are available on Docker Hub, with variants for GPU, CPU, ARM64, NVIDIA Jetson, and Conda. 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 detailed Docker instructions, see the Docker quickstart guide.

How do I clone the Ultralytics repository for development?

Clone the Ultralytics repository and set up a development environment with:

`# 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 allows contributions to the project or experimentation with the latest source code. For details, visit the Ultralytics GitHub repository.

Why should I use Ultralytics YOLO CLI?

The Ultralytics YOLO CLI simplifies running object detection tasks without Python code, enabling single-line commands for training, validation, and prediction directly from your terminal. The basic syntax is:

For example, to train a detection model:

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

Explore more commands and usage examples in the full CLI Guide.