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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.

Example

Ultralytics yolo commands use the following syntax: 

yolo TASK MODE ARGS

Where   TASK (optional) is one of [detect, segment, classify]
        MODE (required) is one of [train, val, predict, export, track]
        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=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01

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

yolo predict model=yolov8n-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=yolov8n.pt data=coco128.yaml batch=1 imgsz=640

Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required) 

yolo export model=yolov8n-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]. If it is not passed explicitly YOLOv8 will try to guess the TASK from the model type.
  • MODE (required) is one of [train, val, predict, export, track]
  • 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 GitHub source.

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=yolov8n.pt imgsz=640 conf=0.25   ✅
  • yolo predict model yolov8n.pt imgsz 640 conf 0.25   ❌
  • yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25   ❌

Train

Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

Example

Start training YOLOv8n on COCO128 for 100 epochs at image-size 640. 

yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640

Resume an interrupted training. 

yolo detect train resume model=last.pt

Val

Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.

Example

Validate an official YOLOv8n model. 

yolo detect val model=yolov8n.pt

Validate a custom-trained model. 

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

Predict

Use a trained YOLOv8n model to run predictions on images.

Example

Predict with an official YOLOv8n model. 

yolo detect predict model=yolov8n.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 YOLOv8n model to a different format like ONNX, CoreML, etc.

Example

Export an official YOLOv8n model to ONNX format. 

yolo export model=yolov8n.pt format=onnx

Export a custom-trained model to ONNX format. 

yolo export model=path/to/best.pt format=onnx

Available YOLOv8 export formats are in the table below. You can export to any format using the format argument, i.e. format='onnx' or format='engine'.

Format format Argument Model Metadata Arguments
PyTorch - yolov8n.pt ✅ -
TorchScript torchscript yolov8n.torchscript ✅ imgsz, optimize
ONNX onnx yolov8n.onnx ✅ imgsz, half, dynamic, simplify, opset
OpenVINO openvino yolov8n_openvino_model/ ✅ imgsz, half
TensorRT engine yolov8n.engine ✅ imgsz, half, dynamic, simplify, workspace
CoreML coreml yolov8n.mlpackage ✅ imgsz, half, int8, nms
TF SavedModel saved_model yolov8n_saved_model/ ✅ imgsz, keras
TF GraphDef pb yolov8n.pb ❌ imgsz
TF Lite tflite yolov8n.tflite ✅ imgsz, half, int8
TF Edge TPU edgetpu yolov8n_edgetpu.tflite ✅ imgsz
TF.js tfjs yolov8n_web_model/ ✅ imgsz
PaddlePaddle paddle yolov8n_paddle_model/ ✅ imgsz
ncnn ncnn yolov8n_ncnn_model/ ✅ imgsz, half

Overriding default arguments

Default arguments can be overridden by simply passing them as arguments in the CLI in arg=value pairs.

Train a detection model for 10 epochs with learning_rate of 0.01 

yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01

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

yolo segment predict model=yolov8n-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=yolov8n.pt data=coco128.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:

yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320

Created 2023-03-12, Updated 2023-09-10
Authors: glenn-jocher (11), shuizhuyuanluo@126.com (1), Laughing-q (1), sergiuwaxmann (1)

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