## 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("yolov8n.yaml")
model(img_tensor) # Or model.forward(). inference.
model.train(data="coco128.yaml", epochs=5)
```
=== "Training pretrained"
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt") # pass any model type
model(...) # inference
model.train(epochs=5)
```
=== "Resume Training"
```python
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
```
=== "Visualize/save Predictions"
```python
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", view_img=True) # Display preds. Accepts all yolo predict arguments
```
!!! note "Export and Deployment"
=== "Export, Fuse & info"
```python
from ultralytics import YOLO
model = YOLO("model.pt")
model.fuse()
model.info(verbose=True) # Print model information
model.export(format=) # TODO:
```
=== "Deployment"
More functionality coming soon
To know more about using `YOLO` models, refer Model class refernce
[Model reference ](reference/model.md ){ .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 = yolo.DetectionTrainer(data=..., epochs=1, device="1,2,3,4") # DDP
trainer.train()
```
=== "SegmentationTrainer"
```python
from ultralytics import yolo
trainer = yolo.SegmentationTrainer(data=..., epochs=1) # override default configs
trainer = yolo.SegmentationTrainer(data=..., epochs=1, device="0,1,2,3") # DDP
trainer.train()
```
=== "ClassificationTrainer"
```python
from ultralytics import yolo
trainer = yolo.ClassificationTrainer(data=..., epochs=1) # override default configs
trainer = yolo.ClassificationTrainer(data=..., epochs=1, device="0,1,2,3") # DDP
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}
