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Update FastSAM and SAM docs (#14499)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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14
docs/en/models/fast-sam.md
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@ -81,19 +81,19 @@ To perform object detection on an image, use the `predict` method as shown below
prompt_process = FastSAMPrompt(source, everything_results, device="cpu")
# Everything prompt
ann = prompt_process.everything_prompt()
results = prompt_process.everything_prompt()
# Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
results = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
# Text prompt
ann = prompt_process.text_prompt(text="a photo of a dog")
results = prompt_process.text_prompt(text="a photo of a dog")
# Point prompt
# points default [[0,0]] [[x1,y1],[x2,y2]]
# point_label default [0] [1,0] 0:background, 1:foreground
ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
prompt_process.plot(annotations=ann, output="./")
results = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
prompt_process.plot(annotations=results, output="./")
```
=== "CLI"
@ -105,6 +105,10 @@ To perform object detection on an image, use the `predict` method as shown below
This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
!!! Note
All the returned `results` in above examples are [Results](../modes/predict.md#working-with-results) object which allows access predicted masks and source image easily.
### Val Usage
Validation of the model on a dataset can be done as follows:

8
docs/en/models/sam.md
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@ -56,10 +56,10 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
model.info()
# Run inference with bboxes prompt
model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
results = model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
# Run inference with points prompt
model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
results = model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
```
!!! Example "Segment everything"
@ -128,6 +128,10 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
results = predictor(source="ultralytics/assets/zidane.jpg", crop_n_layers=1, points_stride=64)
```
!!! Note
All the returned `results` in above examples are [Results](../modes/predict.md#working-with-results) object which allows access predicted masks and source image easily.
- More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md).
## SAM comparison vs YOLOv8