Apply Non-Maximum Suppression (NMS)

You can apply Non-Maximum Suppression (NMS) to predictions from a detection model to remove overlapping bounding boxes.

To do so, add .with_nms() to the result of any predict() or predict_sahi() method from an object detection model.

Here is an example of running NMS on predictions from a Grounding DINO model:

Without NMSWith NMS

from autodistill_owlv2 import OWLv2
from autodistill.detection import CaptionOntology
from autodistill.utils import plot

import cv2

ontology = CaptionOntology({"person": "person"})

base_model = OWLv2(ontology=ontology)

detections = base_model.predict("./dog.jpeg")

plot(
    image=cv2.imread("./dog.jpeg"),
    detections=detections,
    classes=base_model.ontology.classes(),
)

Without NMS

from autodistill_owlv2 import OWLv2
from autodistill.detection import CaptionOntology
from autodistill.utils import plot

import cv2

ontology = CaptionOntology({"person": "person"})

base_model = OWLv2(ontology=ontology)

detections = base_model.predict("./dog.jpeg")

plot(
    image=cv2.imread("./dog.jpeg"),
    detections=detections.with_nms(),
    classes=base_model.ontology.classes(),
)

With NMS