## 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 [Model reference](#){ .md-button .md-button--primary} --- ### 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}