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ultralytics 8.3.13 SAM prompt-inference refactor (#16894)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2024-10-14 23:48:47 +08:00 • committed by GitHub
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15
docs/en/models/sam-2.md
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@ -142,11 +142,20 @@ SAM 2 can be utilized across a broad spectrum of tasks, including real-time vide
# Display model information (optional)
model.info()
# Segment with bounding box prompt
# Run inference with bboxes prompt
results = model("path/to/image.jpg", bboxes=[100, 100, 200, 200])
# Segment with point prompt
results = model("path/to/image.jpg", points=[150, 150], labels=[1])
# Run inference with single point
results = model(points=[900, 370], labels=[1])
# Run inference with multiple points
results = model(points=[[400, 370], [900, 370]], labels=[1, 1])
# Run inference with multiple points prompt per object
results = model(points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
# Run inference with negative points prompt
results = model(points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
```
#### Segment Everything

8
docs/en/models/sam.md
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@ -59,16 +59,16 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
results = model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
# Run inference with single point
results = predictor(points=[900, 370], labels=[1])
results = model(points=[900, 370], labels=[1])
# Run inference with multiple points
results = predictor(points=[[400, 370], [900, 370]], labels=[1, 1])
results = model(points=[[400, 370], [900, 370]], labels=[1, 1])
# Run inference with multiple points prompt per object
results = predictor(points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
results = model(points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
# Run inference with negative points prompt
results = predictor(points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
results = model(points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
```
!!! example "Segment everything"