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71 lines
2.2 KiB
Markdown
71 lines
2.2 KiB
Markdown
## Using YOLO models
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This is the simplest way of simply using yolo models in a python environment. It can be imported from the `ultralytics` module.
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!!! example "Usage"
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=== "Training"
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```python
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from ultralytics import YOLO
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model = YOLO()
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model.new("n.yaml") # pass any model type
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model.train(data="coco128.yaml", epochs=5)
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```
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=== "Training pretrained"
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```python
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from ultralytics import YOLO
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model = YOLO()
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model.load("n.pt") # pass any model type
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model(...) # inference
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model.train(data="coco128.yaml", epochs=5)
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```
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=== "Resume Training"
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```python
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from ultralytics import YOLO
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model = YOLO()
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model.resume(task="detect") # resume last detection training
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model.resume(task="detect", model="last.pt") # resume from a given model
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```
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More functionality coming soon
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To know more about using `YOLO` models, refer Model class refernce
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[Model reference](#){ .md-button .md-button--primary}
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---
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### Customizing Tasks with Trainers
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`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.
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You can easily cusotmize Trainers to support custom tasks or explore R&D ideas.
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!!! tip "Trainer Examples"
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=== "DetectionTrainer"
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```python
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from ultralytics import yolo
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trainer = yolo.DetectionTrainer(data=..., epochs=1) # override default configs
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trainer.train()
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```
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=== "SegmentationTrainer"
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```python
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from ultralytics import yolo
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trainer = yolo.SegmentationTrainer(data=..., epochs=1) # override default configs
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trainer.train()
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```
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=== "ClassificationTrainer"
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```python
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from ultralytics import yolo
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trainer = yolo.ClassificationTrainer(data=..., epochs=1) # override default configs
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trainer.train()
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```
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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.
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[Customization tutorials](#){ .md-button .md-button--primary}
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