FiftyOne integration

The FiftyOne integration transfers predictions and datasets between LibreYOLO and FiftyOne.

Install

bash

pip install "libreyolo[fiftyone]"

FiftyOne remains outside all. Its headless OpenCV package shares the cv2 module with the core install. Use a separate environment or reinstall the GUI OpenCV package if display windows are needed. FiftyOne starts a local database and needs write access to its user directory.

Apply a model

Python

import fiftyone as fofrom libreyolo import SAMPLE_IMAGEfrom libreyolo.integrations.fiftyone import apply_model dataset = fo.Dataset()dataset.add_sample(fo.Sample(filepath=str(SAMPLE_IMAGE)))apply_model(dataset, "LibreYOLO9s.pt", label_field="predictions", device="cpu")

apply_model(dataset, model, label_field="predictions", batch_size=...) accepts a loaded model or checkpoint name. It forwards prediction thresholds and image-size arguments. to_fiftyone_model() returns a wrapper for other FiftyOne model APIs.

Label mapping

Detection writes boxes; instance segmentation attaches masks or polylines. OBB writes closed polylines. Pose writes keypoints and detections in separate fields. Classification writes the top-1 label. Coordinates normalize against the original image; tracking IDs become Detection.index.

Dataset conversion

to_fiftyone(data, split="val") imports YOLO-layout or COCO-JSON dataset YAMLs. from_fiftyone(view, export_dir, split="val", classes=...) writes a dataset YAML and labels. Call it once with split="train" and once with split="val" into the same directory to get both splits in one YAML. Pass classes= to keep class IDs stable across filtered views.