Segment and Annotate

Ultralytics instance segmentation adds a mask to every detected instance. This pipeline keeps the native result intact until the final plotting step, where Ultralytics renders masks, boxes, labels, and confidence values.

Before	After

Build the pipeline

Use a segmentation checkpoint and select the one result for the input image. color_mode="instance" gives each detected instance its own mask colour.

from ml_pipes.core import Pipeline
from ml_pipes.standard import Select
from ml_pipes.ultralytics import results, yolo

pipeline = Pipeline(
    [
        yolo.Predict(model="yolo26n-seg.pt", conf=0.25),
        Select(0),
        results.Plot(
            boxes=True,
            labels=True,
            conf=True,
            color_mode="instance",
            save=True,
            filename="segmented.jpg",
        ),
    ],
    auto_validate=True,
)

Run it on an image

The call boundary contains data only: model and plot configuration were fixed when the pipeline was built.

import cv2

image = cv2.imread("bus.jpg")
if image is None:
    raise RuntimeError("Could not load bus.jpg")

annotated = pipeline(image)
cv2.imwrite("segmented-copy.jpg", annotated)

Run the example

python examples/run_segment_and_annotate.py
python examples/run_segment_and_annotate.py --source path/to/image.jpg --show

See run_segment_and_annotate.py for all command-line options.