comments: true
description: >-
Explore the YOLO11 command line interface (CLI) for easy execution of
detection tasks without needing a Python environment.
keywords: >-
YOLO11 CLI, command line interface, YOLO11 commands, detection tasks,
Ultralytics, model training, model prediction
Command Line Interface Usage
The YOLO 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.
Watch: Mastering Ultralytics YOLO: CLI
!!! example
=== "Syntax"
Ultralytics `yolo` commands use the following syntax:
```bash
yolo TASK MODE ARGS
Where TASK (optional) is one of [detect, segment, classify, pose, obb]
MODE (required) is one of [train, val, predict, export, track, benchmark]
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
```
See all ARGS in the full [Configuration Guide](cfg.md) or with `yolo cfg`
=== "Train"
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
```bash
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
=== "Predict"
Predict a YouTube video using a pretrained segmentation model at image size 320:
```bash
yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Val a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
=== "Export"
Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
```bash
yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
```
=== "Special"
Run special commands to see version, view settings, run checks and more:
```bash
yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg
```
Where:
TASK(optional) is one of[detect, segment, classify, pose, obb]. If it is not passed explicitly YOLO11 will try to guess theTASKfrom the model type.MODE(required) is one of[train, val, predict, export, track, benchmark]ARGS(optional) are any number of customarg=valuepairs likeimgsz=320that override defaults. For a full list of availableARGSsee the Configuration page anddefaults.yaml
!!! 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` ❌
- `yolo predict --model yolo11n.pt --imgsz 640 --conf 0.25` ❌
Train
Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.
!!! example
=== "Train"
Start training YOLO11n on COCO8 for 100 epochs at image-size 640.
```bash
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
=== "Resume"
Resume an interrupted training.
```bash
yolo detect train resume model=last.pt
```
Val
Validate trained YOLO11n model accuracy on the COCO8 dataset. No arguments are needed as the model retains its training data and arguments as model attributes.
!!! example
=== "Official"
Validate an official YOLO11n model.
```bash
yolo detect val model=yolo11n.pt
```
=== "Custom"
Validate a custom-trained model.
```bash
yolo detect val model=path/to/best.pt
```
Predict
Use a trained YOLO11n model to run predictions on images.
!!! example
=== "Official"
Predict with an official YOLO11n model.
```bash
yolo detect predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
=== "Custom"
Predict with a custom model.
```bash
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'
```
Export
Export a YOLO11n model to a different format like ONNX, CoreML, etc.
!!! example
=== "Official"
Export an official YOLO11n model to ONNX format.
```bash
yolo export model=yolo11n.pt format=onnx
```
=== "Custom"
Export a custom-trained model to ONNX format.
```bash
yolo export model=path/to/best.pt format=onnx
```
Available YOLO11 export formats are in the table below. You can export to any format using the format argument, i.e. format='onnx' or format='engine'.
{% include "macros/export-table.md" %}
See full export details in the Export page.
Overriding default arguments
Default arguments can be overridden by simply passing them as arguments in the CLI in arg=value pairs.
!!! tip
=== "Train"
Train a detection model for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
=== "Predict"
Predict a YouTube video using a pretrained segmentation model at image size 320:
```bash
yolo segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Validate a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
Overriding default config file
You can override the default.yaml config file entirely by passing a new file with the cfg arguments, i.e. cfg=custom.yaml.
To do this first create a copy of default.yaml in your current working dir with the yolo copy-cfg command.
This will create default_copy.yaml, which you can then pass as cfg=default_copy.yaml along with any additional args, like imgsz=320 in this example:
!!! example
=== "CLI"
```bash
yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320
```
FAQ
How do I use the Ultralytics YOLO11 command line interface (CLI) for model training?
To train a YOLO11 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, you would run:
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
This command uses the train mode with specific arguments. Refer to the full list of available arguments in the Configuration Guide.
What tasks can I perform with the Ultralytics YOLO11 CLI?
The Ultralytics YOLO11 CLI supports a variety of tasks including detection, segmentation, classification, validation, prediction, export, and tracking. For instance:
- Train a Model: Run
yolo train data=<data.yaml> model=<model.pt> epochs=<num>. - Run Predictions: Use
yolo predict model=<model.pt> source=<data_source> imgsz=<image_size>. - Export a Model: Execute
yolo export model=<model.pt> format=<export_format>.
Each task can be customized with various arguments. For detailed syntax and examples, see the respective sections like Train, Predict, and Export.
How can I validate the accuracy of a trained YOLO11 model using the CLI?
To validate a YOLO11 model's accuracy, use the val mode. For example, to validate a pretrained detection model with a batch size of 1 and image size of 640, run:
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
This command evaluates the model on the specified dataset and provides performance metrics. For more details, refer to the Val section.
What formats can I export my YOLO11 models to using the CLI?
YOLO11 models can be exported to various formats such as ONNX, CoreML, TensorRT, and more. For instance, to export a model to ONNX format, run:
yolo export model=yolo11n.pt format=onnx
For complete details, visit the Export page.
How do I customize YOLO11 CLI commands to override default arguments?
To override default arguments in YOLO11 CLI commands, pass them as arg=value pairs. For example, to train a model with custom arguments, use:
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
For a full list of available arguments and their descriptions, refer to the Configuration Guide. Ensure arguments are formatted correctly, as shown in the Overriding default arguments section.