![]() ![]() Track mode is available for all Detect, Segment and Pose models.Īll Models download automatically from the latest Ultralytics release on first use. ![]() YOLOv8 Detect, Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLOv8 Classify models pretrained on the ImageNet dataset. See YOLOv8 Python Docs for more examples. export ( format = "onnx" ) # export the model to ONNX format val () # evaluate model performance on the validation set results = model ( "" ) # predict on an image path = model. train ( data = "coco128.yaml", epochs = 3 ) # train the model metrics = model. YOLOv8 may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above: 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) # Use the model model. Yolo can be used for a variety of tasks and modes and accepts additional arguments, i.e. YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command: yolo predict model =yolov8n.pt source = '' Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.įor alternative installation methods including Conda, Docker, and Git, please refer to the Quickstart Guide. See below for a quickstart installation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment. ![]()
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