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Overview

The Detections class encapsulates a wide array of detection data. The class can represent bounding box, segmentation, and classification results.

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Arguments

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detection_type
DetectionType
required
Type of detection (DetectionType.BOUNDING_BOX, DetectionType.SEGMENTATION, or DetectionType.CLASSIFICATION).
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xyxy
np.ndarray[np.float32]
required
Coordinates of bounding boxes [x1, y1, x2, y2] for each detection, where (x1, y1) refers to the top left coordinate and (x2, y2) refers to the bottom right coordinate.
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class_ids
np.ndarray[np.int32]]
required
Class IDs for each detection, indexing into classes.
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classes
np.ndarray[np.object_]
required
Array of class names, indexed by class_ids.
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data
Dict[str, Union[np.ndarray, List]]
Additional custom data related to detections (Default: empty dictionary).
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masks
np.ndarray[np.float32]
Segmentation masks for each detection, required for segmentation types.
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confidence
np.ndarray[np.float32]
Confidence scores for each detection.

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Examples

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Bounding Box Detections

bounding_box_detections = Detections(
    xyxy=np.array([[50, 50, 100, 100], [150, 150, 200, 200]]),
    detection_type=DetectionType.BOUNDING_BOX,
    class_ids=np.array([0, 1]),
    classes=np.array(["cat", "dog"]),
    confidence=np.array([0.9, 0.8])
)
There are two bounding boxes in this bounding box Detections object: the box with coordinates (50, 50) and (100, 100) is of class “cat” with 90% confidence, and the box with coordinates (150, 150) and (200, 200) is of class “dog” with 80% confidence.

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Classification Detections

classification_detections = Detections.from_classification(
    assigned_classes=["apple", "fruit"]
    all_classes=["apple", "orange", "banana", "fruit", "vegetable"]
)
A classification detection is created with multiple classifications: “apple” and “fruit”. The resulting Detections object looks like this: 
Detections(
      xyxy=np.zeros((len(assigned_classes), 4)),
      class_ids=np.array([all_classes.index(c) for c in assigned_classes]),
      classes=np.array(all_classes),
      detection_type=DetectionType.CLASSIFICATION
)

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Segmentation Detections

We will support Segmentation detections shortly.

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Methods

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Detections.split()
Splits the Detections object into a list of Detections objects, each containing a single detection.Returns:
  • List['Detections']: A list of Detections objects, each containing a single detection.
splits = detections.split()
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Detections.from_classification(assigned_classes)
Creates a Detections instance from a list of class namesParameters:
  • assigned_classes (List[str]): The class name(s) that have been assigned to the classification detection.
  • all_classes (Optional[List[str]]): List of strings representing all possible class names that can be assigned in the classification detection.
Returns:
  • Detections: A classification Detections object.
classification_detections = Detections.from_classification(
   assigned_classes=["cat"],
   all_classes=["cat", "dog", "mouse"]
)
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Detections.from_yolov5(yolov5_results)
Creates a Detections instance from a YOLOv5 inference result.Parameters:
  • yolov5_results (yolov5.models.common.Detections): The output Detections instance from YOLOv5.
Returns:
  • Detections: A bounding box Detections object.
import torch

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
result = model("path/to/image.jpg")
detections = Detections.from_yolov5(result)
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Detections.from_ultralytics(ultralytics_results)
Creates a Detections instance from an Ultralytics YOLOv8 inference result.Parameters:
  • ultralytics_results (ultralytics.yolo.engine.results.Results): The output Results instance from YOLOv8.
Returns:
  • Detections: A bounding box or segmentation Detections object.
from ultralytics import YOLO

model = YOLO('yolov8s.pt')
result = model("path/to/image.jpg")[0]
detections = Detections.from_ultralytics(result)
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Detections.empty()
Creates an empty Detections object.Returns:
  • Detections: An empty bounding box Detections object.
empty_detections = Detections.empty()
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Detections.to_supervision()
Converts the Detections instance to a supervision.Detections instance.Returns:
  • sv.Detections: A supervision Detections instance.
sv_detections = detections.to_supervision()

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Properties

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confidence_scores
Returns the list of confidence scores associated with the detections.
scores = detections.confidence_scores
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area
Returns a NumPy array containing the area of each detection.Specifically, if segmentation masks are available, a NumPy array containing the area of each mask is returned. Otherwise, a NumPy array containing the area of each bounding box is returned.
areas = detections.area
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box_area
Returns a NumPy array containing the area of each bounding box.
box_areas = detections.box_area
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class_names
Return the list of class names associated with the detections.
names = detections.class_names

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Example

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Creating a Detection from YOLOv5

import cv2
import torch

image = cv2.imread("path/to/image.jpg")
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
result = model(image)
detections = Detections.from_yolov5(result)

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Converting to Supervision

sv_detections = detections.to_supervision()
This documentation covers the essential usage of the Detections class, including creation, manipulation, and conversion of detection data.