[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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Ayush Chaurasia 2023-01-02 20:42:30 +05:30 committed by GitHub
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@ -6,8 +6,7 @@ This is the simplest way of simply using yolo models in a python environment. It
```python
from ultralytics import YOLO
model = YOLO()
model.new("n.yaml") # pass any model type
model = YOLO("yolov8n.yaml")
model(img_tensor) # Or model.forward(). inference.
model.train(data="coco128.yaml", epochs=5)
```
@ -16,10 +15,9 @@ This is the simplest way of simply using yolo models in a python environment. It
```python
from ultralytics import YOLO
model = YOLO()
model.load("n.pt") # pass any model type
model = YOLO("yolov8n.pt") # pass any model type
model(...) # inference
model.train(data="coco128.yaml", epochs=5)
model.train(epochs=5)
```
=== "Resume Training"
@ -35,8 +33,7 @@ This is the simplest way of simply using yolo models in a python environment. It
```python
from ultralytics import YOLO
model = YOLO()
model.load("model.pt")
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
@ -48,7 +45,7 @@ This is the simplest way of simply using yolo models in a python environment. It
```python
from ultralytics import YOLO
model = YOLO()
model = YOLO("model.pt")
model.fuse()
model.info(verbose=True) # Print model information
model.export(format=) # TODO:
@ -61,7 +58,7 @@ This is the simplest way of simply using yolo models in a python environment. It
To know more about using `YOLO` models, refer Model class refernce
[Model reference](#){ .md-button .md-button--primary}
[Model reference](reference/model.md){ .md-button .md-button--primary}
---
### Customizing Tasks with Trainers