YOLOv8-16bit/docs/sdk.md

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.

!!! example "Usage" === "Training" ```python from ultralytics import YOLO

    model = YOLO()
    model.new("n.yaml") # pass any model type
    model.train(data="coco128.yaml", epochs=5)
    ```

=== "Training pretrained"
    ```python
    from ultralytics import YOLO

    model = YOLO()
    model.load("n.pt") # pass any model type
    model(...) # inference
    model.train(data="coco128.yaml", epochs=5)
    ```

=== "Resume Training"
    ```python
    from ultralytics import YOLO

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

More functionality coming soon

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

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---

Customizing Tasks with Trainers

YOLO model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from BaseTrainer. You can easily cusotmize Trainers to support custom tasks or explore R&D ideas.

!!! tip "Trainer Examples" === "DetectionTrainer" ```python from ultralytics import yolo

    trainer = yolo.DetectionTrainer(data=..., epochs=1) # override default configs
    trainer.train()
    ```

=== "SegmentationTrainer"
    ```python
    from ultralytics import yolo

    trainer = yolo.SegmentationTrainer(data=..., epochs=1) # override default configs
    trainer.train()
    ```
=== "ClassificationTrainer"
    ```python
    from ultralytics import yolo

    trainer = yolo.ClassificationTrainer(data=..., epochs=1) # override default configs
    trainer.train()
    ```

Learn more about Customizing Trainers, Validators and Predictors to suit your project needs in the Customization Section. More details about the base engine classes is available in the reference section.

Customization tutorials{ .md-button .md-button--primary}