ml-pipes-ultralytics 0.1.4

Ultralytics YOLO capabilities as composable operators in ml-pipes.
Build explicit computer-vision pipelines for prediction, embedding, tracking, and result processing.

Install

python -m pip install ml-pipes-ultralytics

See the full installation guide for environment setup and GPU configuration.

Coverage

The package operator catalog is maintained in docs/reference.md. The Ultralytics API comparison is in docs/coverage.md.

License and terms

ml-pipes-ultralytics is licensed under the Apache License 2.0. It requires Ultralytics, which is licensed separately under AGPL-3.0 or an Ultralytics Enterprise License. Installing or using this package does not grant rights to Ultralytics software or model weights. Users of the community Ultralytics distribution must comply with AGPL-3.0; Enterprise users must ensure their Ultralytics agreement covers their intended use.

Quickstart

Build a segmentation-and-annotation pipeline. The model configuration belongs to yolo.Predict; the pipeline call receives only the BGR image.

import cv2

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(color_mode="instance"),
    ]
)

image = cv2.imread("photo.jpg")
annotated = pipeline(image)
cv2.imwrite("annotated.jpg", annotated)

yolo.Predict, yolo.Embed, and yolo.Track accept normal Ultralytics source types—paths, URLs, camera sources, in-memory images, tensors, and batches—but do not support stream=True. For videos or large datasets, use a pipeline that explicitly owns decoding, batching, and frame scheduling.

Documentation

Want to learn how to use Ultralytics with ml-pipes? Explore our documentation, how-to guides, and end-to-end examples!

For additional YOLO examples and broader computer-vision use cases, see ml-pipes-supervision.

Built with Ultralytics x ml-pipes