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Python Interface

Using YOLO models

This is the simplest way of simply using yolo models in a python environment. It can be imported from the ultralytics module.

Usage

from ultralytics import YOLO

model = YOLO("yolov8n.yaml")
model(img_tensor) # Or model.forward(). inference.
model.train(data="coco128.yaml", epochs=5)

from ultralytics import YOLO

model = YOLO("yolov8n.pt") # pass any model type
model(...) # inference
model.train(epochs=5)

from ultralytics import YOLO

model = YOLO()
model.resume(task="detect") # resume last detection training
model.resume(model="last.pt") # resume from a given model/run

from ultralytics import YOLO

model = YOLO("model.pt")
model.predict(source="0") # accepts all formats - img/folder/vid.*(mp4/format). 0 for webcam
model.predict(source="folder", show=True) # Display preds. Accepts all yolo predict arguments

Export and Deployment

from ultralytics import YOLO

model = YOLO("model.pt")
model.fuse()  
model.info(verbose=True)  # Print model information
model.export(format=)  # TODO:

More functionality coming soon

To know more about using YOLO models, refer Model class Reference

Model reference

Using Trainers

YOLO model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from BaseTrainer.

Detection Trainer Example

from ultralytics.yolo import v8 import DetectionTrainer, DetectionValidator, DetectionPredictor

# trainer
trainer = DetectionTrainer(overrides={})
trainer.train()
trained_model = trainer.best

# Validator
val = DetectionValidator(args=...)
val(model=trained_model)

# predictor
pred = DetectionPredictor(overrides={})
pred(source=SOURCE, model=trained_model)

# resume from last weight
overrides["resume"] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)

You can easily customize Trainers to support custom tasks or explore R&D ideas. Learn more about Customizing Trainers, Validators and Predictors to suit your project needs in the Customization Section.

Customization tutorials