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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

Ultralytics yolo commands use the following syntax:

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 or with yolo cfg

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

Where:

  • TASK (optional) is one of [detect, segment, classify, pose, obb]. If it is not passed explicitly YOLO11 will try to guess the TASK from the model type.
  • 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. For a full list of available ARGS see the Configuration page and defaults.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

Start training YOLO11n on COCO8 for 100 epochs at image-size 640.

yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640

Resume an interrupted training.

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

Validate an official YOLO11n model.

yolo detect val model=yolo11n.pt

Validate a custom-trained model.

yolo detect val model=path/to/best.pt

Predict

Use a trained YOLO11n model to run predictions on images.

Example

Predict with an official YOLO11n model.

yolo detect predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'

Predict with a custom model.

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

Export an official YOLO11n model to ONNX format.

yolo export model=yolo11n.pt format=onnx

Export a custom-trained model to ONNX format.

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'.

Formatformat ArgumentModelMetadataArguments
PyTorch-yolo11n.pt✅-
TorchScripttorchscriptyolo11n.torchscript✅imgsz, optimize, batch
ONNXonnxyolo11n.onnx✅imgsz, half, dynamic, simplify, opset, batch
OpenVINOopenvinoyolo11n_openvino_model/✅imgsz, half, int8, batch
TensorRTengineyolo11n.engine✅imgsz, half, dynamic, simplify, workspace, int8, batch
CoreMLcoremlyolo11n.mlpackage✅imgsz, half, int8, nms, batch
TF SavedModelsaved_modelyolo11n_saved_model/✅imgsz, keras, int8, batch
TF GraphDefpbyolo11n.pb❌imgsz, batch
TF Litetfliteyolo11n.tflite✅imgsz, half, int8, batch
TF Edge TPUedgetpuyolo11n_edgetpu.tflite✅imgsz
TF.jstfjsyolo11n_web_model/✅imgsz, half, int8, batch
PaddlePaddlepaddleyolo11n_paddle_model/✅imgsz, batch
MNNmnnyolo11n.mnn✅imgsz, batch, int8, half
NCNNncnnyolo11n_ncnn_model/✅imgsz, half, batch
IMX500imxyolo11n_imx_model/✅imgsz, int8

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 a detection model for 10 epochs with learning_rate of 0.01

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

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

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

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.

📅 Created 1 year ago ✏️ Updated 1 month ago
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