[Docs]: Link buttons, add autobackend, BaseModel and ops (#130)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/sdk.md
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docs/sdk.md
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@ -6,8 +6,7 @@ This is the simplest way of simply using yolo models in a python environment. It
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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 = YOLO("yolov8n.yaml")
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model(img_tensor) # Or model.forward(). inference.
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model.train(data="coco128.yaml", epochs=5)
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```
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@ -16,10 +15,9 @@ This is the simplest way of simply using yolo models in a python environment. It
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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 = YOLO("yolov8n.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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model.train(epochs=5)
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```
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=== "Resume Training"
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@ -35,8 +33,7 @@ This is the simplest way of simply using yolo models in a python environment. It
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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("model.pt")
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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", view_img=True) # Display preds. Accepts all yolo predict arguments
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@ -48,7 +45,7 @@ This is the simplest way of simply using yolo models in a python environment. It
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```python
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from ultralytics import YOLO
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model = 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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@ -61,7 +58,7 @@ This is the simplest way of simply using yolo models in a python environment. It
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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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[Model reference](reference/model.md){ .md-button .md-button--primary}
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---
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### Customizing Tasks with Trainers
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