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141 lines
3.9 KiB
141 lines
3.9 KiB
This is the simplest way of simply using YOLOv8 models in a Python environment. It can be imported from
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the `ultralytics` module.
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!!! example "Train"
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=== "From pretrained(recommanded)"
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.pt") # pass any model type
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model.train(epochs=5)
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```
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=== "From scratch"
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml")
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model.train(data="coco128.yaml", epochs=5)
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```
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=== "Resume"
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```python
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TODO: Resume feature is under development and should be released soon.
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```
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!!! example "Val"
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=== "Val after training"
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml")
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model.train(data="coco128.yaml", epochs=5)
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model.val() # It'll automatically evaluate the data you trained.
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```
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=== "Val independently"
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```python
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from ultralytics import YOLO
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model = YOLO("model.pt")
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# It'll use the data yaml file in model.pt if you don't set data.
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model.val()
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# or you can set the data you want to val
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model.val(data="coco128.yaml")
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```
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!!! example "Predict"
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=== "From source"
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```python
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from ultralytics import YOLO
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model = YOLO("model.pt")
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model.predict(source="0") # accepts all formats - img/folder/vid.*(mp4/format). 0 for webcam
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model.predict(source="folder", show=True) # Display preds. Accepts all yolo predict arguments
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```
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=== "From image/ndarray/tensor"
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```python
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# TODO, still working on it.
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```
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=== "Return outputs"
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```python
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from ultralytics import YOLO
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model = YOLO("model.pt")
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outputs = model.predict(source="0", return_outputs=True) # treat predict as a Python generator
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for output in outputs:
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# each output here is a dict.
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# for detection
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print(output["det"]) # np.ndarray, (N, 6), xyxy, score, cls
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# for segmentation
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print(output["det"]) # np.ndarray, (N, 6), xyxy, score, cls
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print(output["segment"]) # List[np.ndarray] * N, bounding coordinates of masks
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# for classify
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print(output["prob"]) # np.ndarray, (num_class, ), cls prob
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```
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!!! note "Export and Deployment"
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=== "Export, Fuse & info"
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```python
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from ultralytics import YOLO
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model = YOLO("model.pt")
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model.fuse()
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model.info(verbose=True) # Print model information
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model.export(format=) # TODO:
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```
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=== "Deployment"
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More functionality coming soon
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To know more about using `YOLO` models, refer Model class Reference
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[Model reference](reference/model.md){ .md-button .md-button--primary}
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---
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### Using 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
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from `BaseTrainer`.
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!!! tip "Detection Trainer Example"
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```python
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from ultralytics.yolo import v8 import DetectionTrainer, DetectionValidator, DetectionPredictor
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# trainer
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trainer = DetectionTrainer(overrides={})
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trainer.train()
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trained_model = trainer.best
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# Validator
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val = DetectionValidator(args=...)
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val(model=trained_model)
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# predictor
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pred = DetectionPredictor(overrides={})
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pred(source=SOURCE, model=trained_model)
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# resume from last weight
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overrides["resume"] = trainer.last
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trainer = detect.DetectionTrainer(overrides=overrides)
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```
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You can easily customize Trainers to support custom tasks or explore R&D ideas.
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Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization
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Section.
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[Customization tutorials](engine.md){ .md-button .md-button--primary}
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