--- comments: true description: 'Learn how to use Ultralytics YOLO through Command Line: train models, run predictions and exports models to different formats easily using terminal commands.' keywords: Ultralytics, YOLO, CLI, train, validation, prediction, command line interface, YOLO CLI, YOLO terminal, model training, prediction, exporting --- # 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 YOLOv8: CLI & Python Usage and Live Inference

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Example === "Syntax" Ultralytics `yolo` commands use the following syntax: ```bash 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](cfg.md) or with `yolo cfg` === "Train" Train a detection model for 10 epochs with an initial learning_rate of 0.01 ```bash yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01 ``` === "Predict" Predict a YouTube video using a pretrained segmentation model at image size 320: ```bash yolo predict model=yolov8n-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=yolov8n.pt data=coco128.yaml batch=1 imgsz=640 ``` === "Export" Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required) ```bash yolo export model=yolov8n-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]`. 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](cfg.md) page and `defaults.yaml` GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/default.yaml). !!! Warning "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](cfg.md) page. !!! Example "Example" === "Train" Start training YOLOv8n on COCO128 for 100 epochs at image-size 640. ```bash yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 ``` === "Resume" Resume an interrupted training. ```bash 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 "Example" === "Official" Validate an official YOLOv8n model. ```bash yolo detect val model=yolov8n.pt ``` === "Custom" Validate a custom-trained model. ```bash yolo detect val model=path/to/best.pt ``` ## Predict Use a trained YOLOv8n model to run predictions on images. !!! Example "Example" === "Official" Predict with an official YOLOv8n model. ```bash yolo detect predict model=yolov8n.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 YOLOv8n model to a different format like ONNX, CoreML, etc. !!! Example "Example" === "Official" Export an official YOLOv8n model to ONNX format. ```bash yolo export model=yolov8n.pt format=onnx ``` === "Custom" Export a custom-trained model to ONNX format. ```bash 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](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` | | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` | | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` | | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` | | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` | | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` | | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` | | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` | | [ncnn](https://github.com/Tencent/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. !!! Tip "" === "Train" Train a detection model for `10 epochs` with `learning_rate` of `0.01` ```bash yolo detect train data=coco128.yaml model=yolov8n.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=yolov8n-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=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: !!! Example === "CLI" ```bash yolo copy-cfg yolo cfg=default_copy.yaml imgsz=320 ```