Install YOLOv8 via the
ultralytics pip package for the latest stable release or by cloning
the https://github.com/ultralytics/ultralytics repository for the most
Git clone method (for development)
Use with CLI
The YOLO command line interface (CLI) lets you simply train, validate or infer models on various tasks and versions.
CLI requires no customization or code. You can simply run all tasks from the terminal with the
Use with Python
Python usage allows users to easily use YOLOv8 inside their Python projects. It provides functions for loading and running the model, as well as for processing the model's output. The interface is designed to be easy to use, so that users can quickly implement object detection in their projects.
Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or classification into their Python projects using YOLOv8.
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 results = model.train(data="coco128.yaml", epochs=3) # train the model results = model.val() # evaluate model performance on the validation set results = model("https://ultralytics.com/images/bus.jpg") # predict on an image success = model.export(format="onnx") # export the model to ONNX format