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Reference for ultralytics/models/fastsam/model.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/fastsam/model.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.fastsam.model.FastSAM

FastSAM(model='FastSAM-x.pt')

Bases: Model

FastSAM model interface.

Example 
from ultralytics import FastSAM

model = FastSAM("last.pt")
results = model.predict("ultralytics/assets/bus.jpg")
Source code in ultralytics/models/fastsam/model.py 
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def __init__(self, model="FastSAM-x.pt"):
    """Call the __init__ method of the parent class (YOLO) with the updated default model."""
    if str(model) == "FastSAM.pt":
        model = "FastSAM-x.pt"
    assert Path(model).suffix not in {".yaml", ".yml"}, "FastSAM models only support pre-trained models."
    super().__init__(model=model, task="segment")

task_map property 

task_map

Returns a dictionary mapping segment task to corresponding predictor and validator classes.

predict

predict(
    source,
    stream=False,
    bboxes=None,
    points=None,
    labels=None,
    texts=None,
    **kwargs
)

Perform segmentation prediction on image or video source.

Supports prompted segmentation with bounding boxes, points, labels, and texts.

Parameters:

NameTypeDescriptionDefault
sourcestr | Image | ndarray

Input source.

required
streambool

Enable real-time streaming.

False
bboxeslist

Bounding box coordinates for prompted segmentation.

None
pointslist

Points for prompted segmentation.

None
labelslist

Labels for prompted segmentation.

None
textslist

Texts for prompted segmentation.

None
**kwargsAny

Additional keyword arguments.

{}

Returns:

TypeDescription
list

Model predictions.

Source code in ultralytics/models/fastsam/model.py 
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def predict(self, source, stream=False, bboxes=None, points=None, labels=None, texts=None, **kwargs):
    """
    Perform segmentation prediction on image or video source.

    Supports prompted segmentation with bounding boxes, points, labels, and texts.

    Args:
        source (str | PIL.Image | numpy.ndarray): Input source.
        stream (bool): Enable real-time streaming.
        bboxes (list): Bounding box coordinates for prompted segmentation.
        points (list): Points for prompted segmentation.
        labels (list): Labels for prompted segmentation.
        texts (list): Texts for prompted segmentation.
        **kwargs (Any): Additional keyword arguments.

    Returns:
        (list): Model predictions.
    """
    prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
    return super().predict(source, stream, prompts=prompts, **kwargs)



📅 Created 1 year ago ✏️ Updated 2 months ago
jk4e glenn-jocher Burhan-Q