## 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}
