The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting YOLOv8 models. The `yolo` command is used for all actions: !!! example "" === "CLI" ```bash yolo TASK MODE ARGS ``` 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]` - `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/yolo/cfg/default.yaml). !!! note "" Note: Arguments MUST be passed as `arg=val` with an equals sign and a space between `arg=val` pairs - `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 "" === "CLI" ```bash yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 ``` === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolov8n.yaml") # build a new model from scratch model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) # Train the model results = model.train(data="coco128.yaml", epochs=100, imgsz=640) ``` ## 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 "" === "CLI" ```bash yolo detect val model=yolov8n.pt # val official model yolo detect val model=path/to/best.pt # val custom model ``` === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolov8n.pt") # load an official model model = YOLO("path/to/best.pt") # load a custom model # Validate the model results = model.val() # no arguments needed, dataset and settings remembered ``` ## Predict Use a trained YOLOv8n model to run predictions on images. !!! example "" === "CLI" ```bash yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model ``` === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolov8n.pt") # load an official model model = YOLO("path/to/best.pt") # load a custom model # Predict with the model results = model("https://ultralytics.com/images/bus.jpg") # predict on an image ``` ## Export Export a YOLOv8n model to a different format like ONNX, CoreML, etc. !!! example "" === "CLI" ```bash yolo export model=yolov8n.pt format=onnx # export official model yolo export model=path/to/best.pt format=onnx # export custom trained model ``` === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolov8n.pt") # load an official model model = YOLO("path/to/best.pt") # load a custom trained # Export the model model.export(format="onnx") ``` Available YOLOv8 export formats include: | Format | `format=` | Model | |----------------------------------------------------------------------------|--------------------|---------------------------| | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | | [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | | [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | | [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | | [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | | [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | | [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | --- ## Overriding default arguments Default arguments can be overriden by simply passing them as arguments in the CLI in `arg=value` pairs. !!! tip "" === "Example 1" 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 ``` === "Example 2" 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/Zgi9g1ksQHc' imgsz=320 ``` === "Example 3" 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-config` 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-config yolo cfg=default_copy.yaml imgsz=320 ```