Detections

The sv.Detections class in the Supervision library standardizes results from various object detection and segmentation models into a consistent format. This class simplifies data manipulation and filtering, providing a uniform API for integration with Supervision trackers, annotators, and tools.

InferenceUltralyticsTransformers

Use sv.Detections.from_inference method, which accepts model results from both detection and segmentation models.

import cv2
import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8n-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)

Use sv.Detections.from_ultralytics method, which accepts model results from both detection and segmentation models.

import cv2
import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)

Use sv.Detections.from_transformers method, which accepts model results from both detection and segmentation models.

import torch
import supervision as sv
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

image = Image.open(<SOURCE_IMAGE_PATH>)
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
    outputs=outputs, target_sizes=target_size)[0]
detections = sv.Detections.from_transformers(
    transformers_results=results,
    id2label=model.config.id2label)

Attributes:

Name	Type	Description
xyxy	ndarray	An array of shape (n, 4) containing the bounding boxes coordinates in format [x1, y1, x2, y2]
mask	Optional[ndarray]	(Optional[np.ndarray]): An array of shape (n, H, W) containing the segmentation masks.
confidence	Optional[ndarray]	An array of shape (n,) containing the confidence scores of the detections.
class_id	Optional[ndarray]	An array of shape (n,) containing the class ids of the detections.
tracker_id	Optional[ndarray]	An array of shape (n,) containing the tracker ids of the detections.
data	Dict[str, Union[ndarray, List]]	A dictionary containing additional data where each key is a string representing the data type, and the value is either a NumPy array or a list of corresponding data.

Source code in supervision/detection/core.py

@dataclass
class Detections:
    """
    The `sv.Detections` class in the Supervision library standardizes results from
    various object detection and segmentation models into a consistent format. This
    class simplifies data manipulation and filtering, providing a uniform API for
    integration with Supervision [trackers](/trackers/), [annotators](/detection/annotators/), and [tools](/detection/tools/line_zone/).

    === "Inference"

        Use [`sv.Detections.from_inference`](/detection/core/#supervision.detection.core.Detections.from_inference)
        method, which accepts model results from both detection and segmentation models.

        ```python
        import cv2
        import supervision as sv
        from inference import get_model

        model = get_model(model_id="yolov8n-640")
        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        results = model.infer(image)[0]
        detections = sv.Detections.from_inference(results)
        ```

    === "Ultralytics"

        Use [`sv.Detections.from_ultralytics`](/detection/core/#supervision.detection.core.Detections.from_ultralytics)
        method, which accepts model results from both detection and segmentation models.

        ```python
        import cv2
        import supervision as sv
        from ultralytics import YOLO

        model = YOLO("yolov8n.pt")
        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        results = model(image)[0]
        detections = sv.Detections.from_ultralytics(results)
        ```

    === "Transformers"

        Use [`sv.Detections.from_transformers`](/detection/core/#supervision.detection.core.Detections.from_transformers)
        method, which accepts model results from both detection and segmentation models.

        ```python
        import torch
        import supervision as sv
        from PIL import Image
        from transformers import DetrImageProcessor, DetrForObjectDetection

        processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
        model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

        image = Image.open(<SOURCE_IMAGE_PATH>)
        inputs = processor(images=image, return_tensors="pt")

        with torch.no_grad():
            outputs = model(**inputs)

        width, height = image.size
        target_size = torch.tensor([[height, width]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]
        detections = sv.Detections.from_transformers(
            transformers_results=results,
            id2label=model.config.id2label)
        ```

    Attributes:
        xyxy (np.ndarray): An array of shape `(n, 4)` containing
            the bounding boxes coordinates in format `[x1, y1, x2, y2]`
        mask: (Optional[np.ndarray]): An array of shape
            `(n, H, W)` containing the segmentation masks.
        confidence (Optional[np.ndarray]): An array of shape
            `(n,)` containing the confidence scores of the detections.
        class_id (Optional[np.ndarray]): An array of shape
            `(n,)` containing the class ids of the detections.
        tracker_id (Optional[np.ndarray]): An array of shape
            `(n,)` containing the tracker ids of the detections.
        data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
            data where each key is a string representing the data type, and the value
            is either a NumPy array or a list of corresponding data.
    """  # noqa: E501 // docs

    xyxy: np.ndarray
    mask: Optional[np.ndarray] = None
    confidence: Optional[np.ndarray] = None
    class_id: Optional[np.ndarray] = None
    tracker_id: Optional[np.ndarray] = None
    data: Dict[str, Union[np.ndarray, List]] = field(default_factory=dict)

    def __post_init__(self):
        validate_detections_fields(
            xyxy=self.xyxy,
            mask=self.mask,
            confidence=self.confidence,
            class_id=self.class_id,
            tracker_id=self.tracker_id,
            data=self.data,
        )

    def __len__(self):
        """
        Returns the number of detections in the Detections object.
        """
        return len(self.xyxy)

    def __iter__(
        self,
    ) -> Iterator[
        Tuple[
            np.ndarray,
            Optional[np.ndarray],
            Optional[float],
            Optional[int],
            Optional[int],
            Dict[str, Union[np.ndarray, List]],
        ]
    ]:
        """
        Iterates over the Detections object and yield a tuple of
        `(xyxy, mask, confidence, class_id, tracker_id, data)` for each detection.
        """
        for i in range(len(self.xyxy)):
            yield (
                self.xyxy[i],
                self.mask[i] if self.mask is not None else None,
                self.confidence[i] if self.confidence is not None else None,
                self.class_id[i] if self.class_id is not None else None,
                self.tracker_id[i] if self.tracker_id is not None else None,
                get_data_item(self.data, i),
            )

    def __eq__(self, other: Detections):
        return all(
            [
                np.array_equal(self.xyxy, other.xyxy),
                np.array_equal(self.mask, other.mask),
                np.array_equal(self.class_id, other.class_id),
                np.array_equal(self.confidence, other.confidence),
                np.array_equal(self.tracker_id, other.tracker_id),
                is_data_equal(self.data, other.data),
            ]
        )

    @classmethod
    def from_yolov5(cls, yolov5_results) -> Detections:
        """
        Creates a Detections instance from a
        [YOLOv5](https://github.com/ultralytics/yolov5) inference result.

        Args:
            yolov5_results (yolov5.models.common.Detections):
                The output Detections instance from YOLOv5

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import cv2
            import torch
            import supervision as sv

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
            result = model(image)
            detections = sv.Detections.from_yolov5(result)
            ```
        """
        yolov5_detections_predictions = yolov5_results.pred[0].cpu().cpu().numpy()

        return cls(
            xyxy=yolov5_detections_predictions[:, :4],
            confidence=yolov5_detections_predictions[:, 4],
            class_id=yolov5_detections_predictions[:, 5].astype(int),
        )

    @classmethod
    def from_ultralytics(cls, ultralytics_results) -> Detections:
        """
        Creates a `sv.Detections` instance from a
        [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.

        !!! Note

            `from_ultralytics` is compatible with
            [detection](https://docs.ultralytics.com/tasks/detect/),
            [segmentation](https://docs.ultralytics.com/tasks/segment/), and
            [OBB](https://docs.ultralytics.com/tasks/obb/) models.

        Args:
            ultralytics_results (ultralytics.yolo.engine.results.Results):
                The output Results instance from Ultralytics

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import cv2
            import supervision as sv
            from ultralytics import YOLO

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = YOLO('yolov8s.pt')
            results = model(image)[0]
            detections = sv.Detections.from_ultralytics(results)
            ```

        !!! tip

            Class names values can be accessed using `detections["class_name"]`.
        """  # noqa: E501 // docs

        if hasattr(ultralytics_results, "obb") and ultralytics_results.obb is not None:
            class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
            class_names = np.array([ultralytics_results.names[i] for i in class_id])
            oriented_box_coordinates = ultralytics_results.obb.xyxyxyxy.cpu().numpy()
            return cls(
                xyxy=ultralytics_results.obb.xyxy.cpu().numpy(),
                confidence=ultralytics_results.obb.conf.cpu().numpy(),
                class_id=class_id,
                tracker_id=ultralytics_results.obb.id.int().cpu().numpy()
                if ultralytics_results.obb.id is not None
                else None,
                data={
                    ORIENTED_BOX_COORDINATES: oriented_box_coordinates,
                    CLASS_NAME_DATA_FIELD: class_names,
                },
            )

        class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
        class_names = np.array([ultralytics_results.names[i] for i in class_id])
        return cls(
            xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
            confidence=ultralytics_results.boxes.conf.cpu().numpy(),
            class_id=class_id,
            mask=extract_ultralytics_masks(ultralytics_results),
            tracker_id=ultralytics_results.boxes.id.int().cpu().numpy()
            if ultralytics_results.boxes.id is not None
            else None,
            data={CLASS_NAME_DATA_FIELD: class_names},
        )

    @classmethod
    def from_yolo_nas(cls, yolo_nas_results) -> Detections:
        """
        Creates a Detections instance from a
        [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md)
        inference result.

        Args:
            yolo_nas_results (ImageDetectionPrediction):
                The output Results instance from YOLO-NAS
                ImageDetectionPrediction is coming from
                'super_gradients.training.models.prediction_results'

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import cv2
            from super_gradients.training import models
            import supervision as sv

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = models.get('yolo_nas_l', pretrained_weights="coco")

            result = list(model.predict(image, conf=0.35))[0]
            detections = sv.Detections.from_yolo_nas(result)
            ```
        """
        if np.asarray(yolo_nas_results.prediction.bboxes_xyxy).shape[0] == 0:
            return cls.empty()

        return cls(
            xyxy=yolo_nas_results.prediction.bboxes_xyxy,
            confidence=yolo_nas_results.prediction.confidence,
            class_id=yolo_nas_results.prediction.labels.astype(int),
        )

    @classmethod
    def from_tensorflow(
        cls, tensorflow_results: dict, resolution_wh: tuple
    ) -> Detections:
        """
        Creates a Detections instance from a
        [Tensorflow Hub](https://www.tensorflow.org/hub/tutorials/tf2_object_detection)
        inference result.

        Args:
            tensorflow_results (dict):
                The output results from Tensorflow Hub.

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import tensorflow as tf
            import tensorflow_hub as hub
            import numpy as np
            import cv2

            module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
            model = hub.load(module_handle)
            img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
            result = model(img)
            detections = sv.Detections.from_tensorflow(result)
            ```
        """  # noqa: E501 // docs

        boxes = tensorflow_results["detection_boxes"][0].numpy()
        boxes[:, [0, 2]] *= resolution_wh[0]
        boxes[:, [1, 3]] *= resolution_wh[1]
        boxes = boxes[:, [1, 0, 3, 2]]
        return cls(
            xyxy=boxes,
            confidence=tensorflow_results["detection_scores"][0].numpy(),
            class_id=tensorflow_results["detection_classes"][0].numpy().astype(int),
        )

    @classmethod
    def from_deepsparse(cls, deepsparse_results) -> Detections:
        """
        Creates a Detections instance from a
        [DeepSparse](https://github.com/neuralmagic/deepsparse)
        inference result.

        Args:
            deepsparse_results (deepsparse.yolo.schemas.YOLOOutput):
                The output Results instance from DeepSparse.

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import supervision as sv
            from deepsparse import Pipeline

            yolo_pipeline = Pipeline.create(
                task="yolo",
                model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
             )
            result = yolo_pipeline(<SOURCE IMAGE PATH>)
            detections = sv.Detections.from_deepsparse(result)
            ```
        """  # noqa: E501 // docs

        if np.asarray(deepsparse_results.boxes[0]).shape[0] == 0:
            return cls.empty()

        return cls(
            xyxy=np.array(deepsparse_results.boxes[0]),
            confidence=np.array(deepsparse_results.scores[0]),
            class_id=np.array(deepsparse_results.labels[0]).astype(float).astype(int),
        )

    @classmethod
    def from_mmdetection(cls, mmdet_results) -> Detections:
        """
        Creates a Detections instance from a
        [mmdetection](https://github.com/open-mmlab/mmdetection) and
        [mmyolo](https://github.com/open-mmlab/mmyolo) inference result.

        Args:
            mmdet_results (mmdet.structures.DetDataSample):
                The output Results instance from MMDetection.

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import cv2
            import supervision as sv
            from mmdet.apis import init_detector, inference_detector

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = init_detector(<CONFIG_PATH>, <WEIGHTS_PATH>, device=<DEVICE>)

            result = inference_detector(model, image)
            detections = sv.Detections.from_mmdetection(result)
            ```
        """  # noqa: E501 // docs

        return cls(
            xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
            confidence=mmdet_results.pred_instances.scores.cpu().numpy(),
            class_id=mmdet_results.pred_instances.labels.cpu().numpy().astype(int),
            mask=mmdet_results.pred_instances.masks.cpu().numpy()
            if "masks" in mmdet_results.pred_instances
            else None,
        )

    @classmethod
    def from_transformers(
        cls, transformers_results: dict, id2label: Optional[Dict[int, str]] = None
    ) -> Detections:
        """
        Creates a Detections instance from object detection or segmentation
        [Transformer](https://github.com/huggingface/transformers) inference result.

        Args:
            transformers_results (dict): The output of Transformers model inference. A
                dictionary containing the `scores`, `labels`, `boxes` and `masks` keys.
            id2label (Optional[Dict[int, str]]): A dictionary mapping class IDs to
                class names. If provided, the resulting Detections object will contain
                `class_name` data field with the class names.

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import torch
            import supervision as sv
            from PIL import Image
            from transformers import DetrImageProcessor, DetrForObjectDetection

            processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
            model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

            image = Image.open(<SOURCE_IMAGE_PATH>)
            inputs = processor(images=image, return_tensors="pt")

            with torch.no_grad():
                outputs = model(**inputs)

            width, height = image.size
            target_size = torch.tensor([[height, width]])
            results = processor.post_process_object_detection(
                outputs=outputs, target_sizes=target_size)[0]

            detections = sv.Detections.from_transformers(
                transformers_results=results,
                id2label=model.config.id2label
            )
            ```

        !!! tip

            Class names values can be accessed using `detections["class_name"]`.
        """  # noqa: E501 // docs

        class_ids = transformers_results["labels"].cpu().detach().numpy().astype(int)
        data = {}
        if id2label is not None:
            class_names = np.array([id2label[class_id] for class_id in class_ids])
            data[CLASS_NAME_DATA_FIELD] = class_names
        if "boxes" in transformers_results:
            return cls(
                xyxy=transformers_results["boxes"].cpu().detach().numpy(),
                confidence=transformers_results["scores"].cpu().detach().numpy(),
                class_id=class_ids,
                data=data,
            )
        elif "masks" in transformers_results:
            masks = transformers_results["masks"].cpu().detach().numpy().astype(bool)
            return cls(
                xyxy=mask_to_xyxy(masks),
                mask=masks,
                confidence=transformers_results["scores"].cpu().detach().numpy(),
                class_id=class_ids,
                data=data,
            )
        else:
            raise NotImplementedError(
                "Only object detection and semantic segmentation results are supported."
            )

    @classmethod
    def from_detectron2(cls, detectron2_results) -> Detections:
        """
        Create a Detections object from the
        [Detectron2](https://github.com/facebookresearch/detectron2) inference result.

        Args:
            detectron2_results: The output of a
                Detectron2 model containing instances with prediction data.

        Returns:
            (Detections): A Detections object containing the bounding boxes,
                class IDs, and confidences of the predictions.

        Example:
            ```python
            import cv2
            import supervision as sv
            from detectron2.engine import DefaultPredictor
            from detectron2.config import get_cfg


            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            cfg = get_cfg()
            cfg.merge_from_file(<CONFIG_PATH>)
            cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
            predictor = DefaultPredictor(cfg)

            result = predictor(image)
            detections = sv.Detections.from_detectron2(result)
            ```
        """

        return cls(
            xyxy=detectron2_results["instances"].pred_boxes.tensor.cpu().numpy(),
            confidence=detectron2_results["instances"].scores.cpu().numpy(),
            mask=detectron2_results["instances"].pred_masks.cpu().numpy()
            if hasattr(detectron2_results["instances"], "pred_masks")
            else None,
            class_id=detectron2_results["instances"]
            .pred_classes.cpu()
            .numpy()
            .astype(int),
        )

    @classmethod
    def from_inference(cls, roboflow_result: Union[dict, Any]) -> Detections:
        """
        Create a `sv.Detections` object from the [Roboflow](https://roboflow.com/)
        API inference result or the [Inference](https://inference.roboflow.com/)
        package results. This method extracts bounding boxes, class IDs,
        confidences, and class names from the Roboflow API result and encapsulates
        them into a Detections object.

        Args:
            roboflow_result (dict, any): The result from the
                Roboflow API or Inference package containing predictions.

        Returns:
            (Detections): A Detections object containing the bounding boxes, class IDs,
                and confidences of the predictions.

        Example:
            ```python
            import cv2
            import supervision as sv
            from inference import get_model

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = get_model(model_id="yolov8s-640")

            result = model.infer(image)[0]
            detections = sv.Detections.from_inference(result)
            ```

        !!! tip

            Class names values can be accessed using `detections["class_name"]`.
        """
        with suppress(AttributeError):
            roboflow_result = roboflow_result.dict(exclude_none=True, by_alias=True)
        xyxy, confidence, class_id, masks, trackers, data = process_roboflow_result(
            roboflow_result=roboflow_result
        )

        if np.asarray(xyxy).shape[0] == 0:
            empty_detection = cls.empty()
            empty_detection.data = {CLASS_NAME_DATA_FIELD: np.empty(0)}
            return empty_detection

        return cls(
            xyxy=xyxy,
            confidence=confidence,
            class_id=class_id,
            mask=masks,
            tracker_id=trackers,
            data=data,
        )

    @classmethod
    def from_sam(cls, sam_result: List[dict]) -> Detections:
        """
        Creates a Detections instance from
        [Segment Anything Model](https://github.com/facebookresearch/segment-anything)
        inference result.

        Args:
            sam_result (List[dict]): The output Results instance from SAM

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import supervision as sv
            from segment_anything import (
                sam_model_registry,
                SamAutomaticMaskGenerator
             )

            sam_model_reg = sam_model_registry[MODEL_TYPE]
            sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
            mask_generator = SamAutomaticMaskGenerator(sam)
            sam_result = mask_generator.generate(IMAGE)
            detections = sv.Detections.from_sam(sam_result=sam_result)
            ```
        """

        sorted_generated_masks = sorted(
            sam_result, key=lambda x: x["area"], reverse=True
        )

        xywh = np.array([mask["bbox"] for mask in sorted_generated_masks])
        mask = np.array([mask["segmentation"] for mask in sorted_generated_masks])

        if np.asarray(xywh).shape[0] == 0:
            return cls.empty()

        xyxy = xywh_to_xyxy(xywh=xywh)
        return cls(xyxy=xyxy, mask=mask)

    @classmethod
    def from_azure_analyze_image(
        cls, azure_result: dict, class_map: Optional[Dict[int, str]] = None
    ) -> Detections:
        """
        Creates a Detections instance from [Azure Image Analysis 4.0](
        https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/
        concept-object-detection-40).

        Args:
            azure_result (dict): The result from Azure Image Analysis. It should
                contain detected objects and their bounding box coordinates.
            class_map (Optional[Dict[int, str]]): A mapping ofclass IDs (int) to class
                names (str). If None, a new mapping is created dynamically.

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import requests
            import supervision as sv

            image = open(input, "rb").read()

            endpoint = "https://.cognitiveservices.azure.com/"
            subscription_key = ""

            headers = {
                "Content-Type": "application/octet-stream",
                "Ocp-Apim-Subscription-Key": subscription_key
             }

            response = requests.post(endpoint,
                headers=self.headers,
                data=image
             ).json()

            detections = sv.Detections.from_azure_analyze_image(response)
            ```
        """
        if "error" in azure_result:
            raise ValueError(
                f'Azure API returned an error {azure_result["error"]["message"]}'
            )

        xyxy, confidences, class_ids = [], [], []

        is_dynamic_mapping = class_map is None
        if is_dynamic_mapping:
            class_map = {}

        class_map = {value: key for key, value in class_map.items()}

        for detection in azure_result["objectsResult"]["values"]:
            bbox = detection["boundingBox"]

            tags = detection["tags"]

            x0 = bbox["x"]
            y0 = bbox["y"]
            x1 = x0 + bbox["w"]
            y1 = y0 + bbox["h"]

            for tag in tags:
                confidence = tag["confidence"]
                class_name = tag["name"]
                class_id = class_map.get(class_name, None)

                if is_dynamic_mapping and class_id is None:
                    class_id = len(class_map)
                    class_map[class_name] = class_id

                if class_id is not None:
                    xyxy.append([x0, y0, x1, y1])
                    confidences.append(confidence)
                    class_ids.append(class_id)

        if len(xyxy) == 0:
            return Detections.empty()

        return cls(
            xyxy=np.array(xyxy),
            class_id=np.array(class_ids),
            confidence=np.array(confidences),
        )

    @classmethod
    def from_paddledet(cls, paddledet_result) -> Detections:
        """
        Creates a Detections instance from
            [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)
            inference result.

        Args:
            paddledet_result (List[dict]): The output Results instance from PaddleDet

        Returns:
            Detections: A new Detections object.

        Example:
            ```python
            import supervision as sv
            import paddle
            from ppdet.engine import Trainer
            from ppdet.core.workspace import load_config

            weights = ()
            config = ()

            cfg = load_config(config)
            trainer = Trainer(cfg, mode='test')
            trainer.load_weights(weights)

            paddledet_result = trainer.predict([images])[0]

            detections = sv.Detections.from_paddledet(paddledet_result)
            ```
        """

        if np.asarray(paddledet_result["bbox"][:, 2:6]).shape[0] == 0:
            return cls.empty()

        return cls(
            xyxy=paddledet_result["bbox"][:, 2:6],
            confidence=paddledet_result["bbox"][:, 1],
            class_id=paddledet_result["bbox"][:, 0].astype(int),
        )

    @classmethod
    def from_lmm(
        cls, lmm: Union[LMM, str], result: Union[str, dict], **kwargs
    ) -> Detections:
        """
        Creates a Detections object from the given result string based on the specified
        Large Multimodal Model (LMM).

        Args:
            lmm (Union[LMM, str]): The type of LMM (Large Multimodal Model) to use.
            result (str): The result string containing the detection data.
            **kwargs: Additional keyword arguments required by the specified LMM.

        Returns:
            Detections: A new Detections object.

        Raises:
            ValueError: If the LMM is invalid, required arguments are missing, or
                disallowed arguments are provided.
            ValueError: If the specified LMM is not supported.

        Examples:
            ```python
            import supervision as sv

            paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
            detections = sv.Detections.from_lmm(
                sv.LMM.PALIGEMMA,
                paligemma_result,
                resolution_wh=(1000, 1000),
                classes=['cat', 'dog']
            )
            detections.xyxy
            # array([[250., 250., 750., 750.]])

            detections.class_id
            # array([0])
            ```
        """
        lmm = validate_lmm_parameters(lmm, result, kwargs)

        if lmm == LMM.PALIGEMMA:
            assert isinstance(result, str)
            xyxy, class_id, class_name = from_paligemma(result, **kwargs)
            data = {CLASS_NAME_DATA_FIELD: class_name}
            return cls(xyxy=xyxy, class_id=class_id, data=data)

        if lmm == LMM.FLORENCE_2:
            assert isinstance(result, dict)
            xyxy, labels, mask, xyxyxyxy = from_florence_2(result, **kwargs)
            if len(xyxy) == 0:
                return cls.empty()

            data = {}
            if labels is not None:
                data[CLASS_NAME_DATA_FIELD] = labels
            if xyxyxyxy is not None:
                data[ORIENTED_BOX_COORDINATES] = xyxyxyxy

            return cls(xyxy=xyxy, mask=mask, data=data)

        raise ValueError(f"Unsupported LMM: {lmm}")

    @classmethod
    def empty(cls) -> Detections:
        """
        Create an empty Detections object with no bounding boxes,
            confidences, or class IDs.

        Returns:
            (Detections): An empty Detections object.

        Example:
            ```python
            from supervision import Detections

            empty_detections = Detections.empty()
            ```
        """
        return cls(
            xyxy=np.empty((0, 4), dtype=np.float32),
            confidence=np.array([], dtype=np.float32),
            class_id=np.array([], dtype=int),
        )

    def is_empty(self) -> bool:
        """
        Returns `True` if the `Detections` object is considered empty.
        """
        empty_detections = Detections.empty()
        empty_detections.data = self.data
        return self == empty_detections

    @classmethod
    def merge(cls, detections_list: List[Detections]) -> Detections:
        """
        Merge a list of Detections objects into a single Detections object.

        This method takes a list of Detections objects and combines their
        respective fields (`xyxy`, `mask`, `confidence`, `class_id`, and `tracker_id`)
        into a single Detections object.

        For example, if merging Detections with 3 and 4 detected objects, this method
        will return a Detections with 7 objects (7 entries in `xyxy`, `mask`, etc).

        !!! Note

            When merging, empty `Detections` objects are ignored.

        Args:
            detections_list (List[Detections]): A list of Detections objects to merge.

        Returns:
            (Detections): A single Detections object containing
                the merged data from the input list.

        Example:
            ```python
            import numpy as np
            import supervision as sv

            detections_1 = sv.Detections(
                xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
                class_id=np.array([1, 2]),
                data={'feature_vector': np.array([0.1, 0.2)])}
             )

            detections_2 = sv.Detections(
                xyxy=np.array([[30, 30, 120, 120]]),
                class_id=np.array([1]),
                data={'feature_vector': [np.array([0.3])]}
             )

            merged_detections = Detections.merge([detections_1, detections_2])

            merged_detections.xyxy
            array([[ 15,  15, 100, 100],
                   [200, 200, 300, 300],
                   [ 30,  30, 120, 120]])

            merged_detections.class_id
            array([1, 2, 1])

            merged_detections.data['feature_vector']
            array([0.1, 0.2, 0.3])
            ```
        """
        detections_list = [
            detections for detections in detections_list if not detections.is_empty()
        ]

        if len(detections_list) == 0:
            return Detections.empty()

        for detections in detections_list:
            validate_detections_fields(
                xyxy=detections.xyxy,
                mask=detections.mask,
                confidence=detections.confidence,
                class_id=detections.class_id,
                tracker_id=detections.tracker_id,
                data=detections.data,
            )

        xyxy = np.vstack([d.xyxy for d in detections_list])

        def stack_or_none(name: str):
            if all(d.__getattribute__(name) is None for d in detections_list):
                return None
            if any(d.__getattribute__(name) is None for d in detections_list):
                raise ValueError(f"All or none of the '{name}' fields must be None")
            return (
                np.vstack([d.__getattribute__(name) for d in detections_list])
                if name == "mask"
                else np.hstack([d.__getattribute__(name) for d in detections_list])
            )

        mask = stack_or_none("mask")
        confidence = stack_or_none("confidence")
        class_id = stack_or_none("class_id")
        tracker_id = stack_or_none("tracker_id")

        data = merge_data([d.data for d in detections_list])

        return cls(
            xyxy=xyxy,
            mask=mask,
            confidence=confidence,
            class_id=class_id,
            tracker_id=tracker_id,
            data=data,
        )

    def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
        """
        Calculates and returns the coordinates of a specific anchor point
        within the bounding boxes defined by the `xyxy` attribute. The anchor
        point can be any of the predefined positions in the `Position` enum,
        such as `CENTER`, `CENTER_LEFT`, `BOTTOM_RIGHT`, etc.

        Args:
            anchor (Position): An enum specifying the position of the anchor point
                within the bounding box. Supported positions are defined in the
                `Position` enum.

        Returns:
            np.ndarray: An array of shape `(n, 2)`, where `n` is the number of bounding
                boxes. Each row contains the `[x, y]` coordinates of the specified
                anchor point for the corresponding bounding box.

        Raises:
            ValueError: If the provided `anchor` is not supported.
        """
        if anchor == Position.CENTER:
            return np.array(
                [
                    (self.xyxy[:, 0] + self.xyxy[:, 2]) / 2,
                    (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
                ]
            ).transpose()
        elif anchor == Position.CENTER_OF_MASS:
            if self.mask is None:
                raise ValueError(
                    "Cannot use `Position.CENTER_OF_MASS` without a detection mask."
                )
            return calculate_masks_centroids(masks=self.mask)
        elif anchor == Position.CENTER_LEFT:
            return np.array(
                [
                    self.xyxy[:, 0],
                    (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
                ]
            ).transpose()
        elif anchor == Position.CENTER_RIGHT:
            return np.array(
                [
                    self.xyxy[:, 2],
                    (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
                ]
            ).transpose()
        elif anchor == Position.BOTTOM_CENTER:
            return np.array(
                [(self.xyxy[:, 0] + self.xyxy[:, 2]) / 2, self.xyxy[:, 3]]
            ).transpose()
        elif anchor == Position.BOTTOM_LEFT:
            return np.array([self.xyxy[:, 0], self.xyxy[:, 3]]).transpose()
        elif anchor == Position.BOTTOM_RIGHT:
            return np.array([self.xyxy[:, 2], self.xyxy[:, 3]]).transpose()
        elif anchor == Position.TOP_CENTER:
            return np.array(
                [(self.xyxy[:, 0] + self.xyxy[:, 2]) / 2, self.xyxy[:, 1]]
            ).transpose()
        elif anchor == Position.TOP_LEFT:
            return np.array([self.xyxy[:, 0], self.xyxy[:, 1]]).transpose()
        elif anchor == Position.TOP_RIGHT:
            return np.array([self.xyxy[:, 2], self.xyxy[:, 1]]).transpose()

        raise ValueError(f"{anchor} is not supported.")

    def __getitem__(
        self, index: Union[int, slice, List[int], np.ndarray, str]
    ) -> Union[Detections, List, np.ndarray, None]:
        """
        Get a subset of the Detections object or access an item from its data field.

        When provided with an integer, slice, list of integers, or a numpy array, this
        method returns a new Detections object that represents a subset of the original
        detections. When provided with a string, it accesses the corresponding item in
        the data dictionary.

        Args:
            index (Union[int, slice, List[int], np.ndarray, str]): The index, indices,
                or key to access a subset of the Detections or an item from the data.

        Returns:
            Union[Detections, Any]: A subset of the Detections object or an item from
                the data field.

        Example:
            ```python
            import supervision as sv

            detections = sv.Detections()

            first_detection = detections[0]
            first_10_detections = detections[0:10]
            some_detections = detections[[0, 2, 4]]
            class_0_detections = detections[detections.class_id == 0]
            high_confidence_detections = detections[detections.confidence > 0.5]

            feature_vector = detections['feature_vector']
            ```
        """
        if isinstance(index, str):
            return self.data.get(index)
        if isinstance(index, int):
            index = [index]
        return Detections(
            xyxy=self.xyxy[index],
            mask=self.mask[index] if self.mask is not None else None,
            confidence=self.confidence[index] if self.confidence is not None else None,
            class_id=self.class_id[index] if self.class_id is not None else None,
            tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
            data=get_data_item(self.data, index),
        )

    def __setitem__(self, key: str, value: Union[np.ndarray, List]):
        """
        Set a value in the data dictionary of the Detections object.

        Args:
            key (str): The key in the data dictionary to set.
            value (Union[np.ndarray, List]): The value to set for the key.

        Example:
            ```python
            import cv2
            import supervision as sv
            from ultralytics import YOLO

            image = cv2.imread(<SOURCE_IMAGE_PATH>)
            model = YOLO('yolov8s.pt')

            result = model(image)[0]
            detections = sv.Detections.from_ultralytics(result)

            detections['names'] = [
                 model.model.names[class_id]
                 for class_id
                 in detections.class_id
             ]
            ```
        """
        if not isinstance(value, (np.ndarray, list)):
            raise TypeError("Value must be a np.ndarray or a list")

        if isinstance(value, list):
            value = np.array(value)

        self.data[key] = value

    @property
    def area(self) -> np.ndarray:
        """
        Calculate the area of each detection in the set of object detections.
        If masks field is defined property returns are of each mask.
        If only box is given property return area of each box.

        Returns:
          np.ndarray: An array of floats containing the area of each detection
            in the format of `(area_1, area_2, , area_n)`,
            where n is the number of detections.
        """
        if self.mask is not None:
            return np.array([np.sum(mask) for mask in self.mask])
        else:
            return self.box_area

    @property
    def box_area(self) -> np.ndarray:
        """
        Calculate the area of each bounding box in the set of object detections.

        Returns:
            np.ndarray: An array of floats containing the area of each bounding
                box in the format of `(area_1, area_2, , area_n)`,
                where n is the number of detections.
        """
        return (self.xyxy[:, 3] - self.xyxy[:, 1]) * (self.xyxy[:, 2] - self.xyxy[:, 0])

    def with_nms(
        self, threshold: float = 0.5, class_agnostic: bool = False
    ) -> Detections:
        """
        Performs non-max suppression on detection set. If the detections result
        from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.

        Args:
            threshold (float, optional): The intersection-over-union threshold
                to use for non-maximum suppression. I'm the lower the value the more
                restrictive the NMS becomes. Defaults to 0.5.
            class_agnostic (bool, optional): Whether to perform class-agnostic
                non-maximum suppression. If True, the class_id of each detection
                will be ignored. Defaults to False.

        Returns:
            Detections: A new Detections object containing the subset of detections
                after non-maximum suppression.

        Raises:
            AssertionError: If `confidence` is None and class_agnostic is False.
                If `class_id` is None and class_agnostic is False.
        """
        if len(self) == 0:
            return self

        assert (
            self.confidence is not None
        ), "Detections confidence must be given for NMS to be executed."

        if class_agnostic:
            predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
        else:
            assert self.class_id is not None, (
                "Detections class_id must be given for NMS to be executed. If you"
                " intended to perform class agnostic NMS set class_agnostic=True."
            )
            predictions = np.hstack(
                (
                    self.xyxy,
                    self.confidence.reshape(-1, 1),
                    self.class_id.reshape(-1, 1),
                )
            )

        if self.mask is not None:
            indices = mask_non_max_suppression(
                predictions=predictions, masks=self.mask, iou_threshold=threshold
            )
        else:
            indices = box_non_max_suppression(
                predictions=predictions, iou_threshold=threshold
            )

        return self[indices]

    def with_nmm(
        self, threshold: float = 0.5, class_agnostic: bool = False
    ) -> Detections:
        """
        Perform non-maximum merging on the current set of object detections.

        Args:
            threshold (float, optional): The intersection-over-union threshold
                to use for non-maximum merging. Defaults to 0.5.
            class_agnostic (bool, optional): Whether to perform class-agnostic
                non-maximum merging. If True, the class_id of each detection
                will be ignored. Defaults to False.

        Returns:
            Detections: A new Detections object containing the subset of detections
                after non-maximum merging.

        Raises:
            AssertionError: If `confidence` is None or `class_id` is None and
                class_agnostic is False.

        ![non-max-merging](https://media.roboflow.com/supervision-docs/non-max-merging.png){ align=center width="800" }
        """  # noqa: E501 // docs
        if len(self) == 0:
            return self

        assert (
            self.confidence is not None
        ), "Detections confidence must be given for NMM to be executed."

        if class_agnostic:
            predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
        else:
            assert self.class_id is not None, (
                "Detections class_id must be given for NMM to be executed. If you"
                " intended to perform class agnostic NMM set class_agnostic=True."
            )
            predictions = np.hstack(
                (
                    self.xyxy,
                    self.confidence.reshape(-1, 1),
                    self.class_id.reshape(-1, 1),
                )
            )

        merge_groups = box_non_max_merge(
            predictions=predictions, iou_threshold=threshold
        )

        result = []
        for merge_group in merge_groups:
            unmerged_detections = [self[i] for i in merge_group]
            merged_detections = merge_inner_detections_objects(
                unmerged_detections, threshold
            )
            result.append(merged_detections)

        return Detections.merge(result)

Attributes

area: np.ndarray property

Calculate the area of each detection in the set of object detections. If masks field is defined property returns are of each mask. If only box is given property return area of each box.

Returns:

Type	Description
ndarray	np.ndarray: An array of floats containing the area of each detection in the format of (area_1, area_2, , area_n), where n is the number of detections.

box_area: np.ndarray property

Calculate the area of each bounding box in the set of object detections.

Returns:

Type	Description
ndarray	np.ndarray: An array of floats containing the area of each bounding box in the format of (area_1, area_2, , area_n), where n is the number of detections.

Functions

__getitem__(index)

Get a subset of the Detections object or access an item from its data field.

When provided with an integer, slice, list of integers, or a numpy array, this method returns a new Detections object that represents a subset of the original detections. When provided with a string, it accesses the corresponding item in the data dictionary.

Parameters:

Name	Type	Description	Default
index	Union[int, slice, List[int], ndarray, str]	The index, indices, or key to access a subset of the Detections or an item from the data.	required

Returns:

Type	Description
Union[Detections, List, ndarray, None]	Union[Detections, Any]: A subset of the Detections object or an item from the data field.

Example

import supervision as sv

detections = sv.Detections()

first_detection = detections[0]
first_10_detections = detections[0:10]
some_detections = detections[[0, 2, 4]]
class_0_detections = detections[detections.class_id == 0]
high_confidence_detections = detections[detections.confidence > 0.5]

feature_vector = detections['feature_vector']

Source code in supervision/detection/core.py

def __getitem__(
    self, index: Union[int, slice, List[int], np.ndarray, str]
) -> Union[Detections, List, np.ndarray, None]:
    """
    Get a subset of the Detections object or access an item from its data field.

    When provided with an integer, slice, list of integers, or a numpy array, this
    method returns a new Detections object that represents a subset of the original
    detections. When provided with a string, it accesses the corresponding item in
    the data dictionary.

    Args:
        index (Union[int, slice, List[int], np.ndarray, str]): The index, indices,
            or key to access a subset of the Detections or an item from the data.

    Returns:
        Union[Detections, Any]: A subset of the Detections object or an item from
            the data field.

    Example:
        ```python
        import supervision as sv

        detections = sv.Detections()

        first_detection = detections[0]
        first_10_detections = detections[0:10]
        some_detections = detections[[0, 2, 4]]
        class_0_detections = detections[detections.class_id == 0]
        high_confidence_detections = detections[detections.confidence > 0.5]

        feature_vector = detections['feature_vector']
        ```
    """
    if isinstance(index, str):
        return self.data.get(index)
    if isinstance(index, int):
        index = [index]
    return Detections(
        xyxy=self.xyxy[index],
        mask=self.mask[index] if self.mask is not None else None,
        confidence=self.confidence[index] if self.confidence is not None else None,
        class_id=self.class_id[index] if self.class_id is not None else None,
        tracker_id=self.tracker_id[index] if self.tracker_id is not None else None,
        data=get_data_item(self.data, index),
    )

__iter__()

Iterates over the Detections object and yield a tuple of (xyxy, mask, confidence, class_id, tracker_id, data) for each detection.

Source code in supervision/detection/core.py

def __iter__(
    self,
) -> Iterator[
    Tuple[
        np.ndarray,
        Optional[np.ndarray],
        Optional[float],
        Optional[int],
        Optional[int],
        Dict[str, Union[np.ndarray, List]],
    ]
]:
    """
    Iterates over the Detections object and yield a tuple of
    `(xyxy, mask, confidence, class_id, tracker_id, data)` for each detection.
    """
    for i in range(len(self.xyxy)):
        yield (
            self.xyxy[i],
            self.mask[i] if self.mask is not None else None,
            self.confidence[i] if self.confidence is not None else None,
            self.class_id[i] if self.class_id is not None else None,
            self.tracker_id[i] if self.tracker_id is not None else None,
            get_data_item(self.data, i),
        )

__len__()

Returns the number of detections in the Detections object.

Source code in supervision/detection/core.py

def __len__(self):
    """
    Returns the number of detections in the Detections object.
    """
    return len(self.xyxy)

__setitem__(key, value)

Set a value in the data dictionary of the Detections object.

Parameters:

Name	Type	Description	Default
key	str	The key in the data dictionary to set.	required
value	Union[ndarray, List]	The value to set for the key.	required

Example

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s.pt')

result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)

detections['names'] = [
     model.model.names[class_id]
     for class_id
     in detections.class_id
 ]

Source code in supervision/detection/core.py

def __setitem__(self, key: str, value: Union[np.ndarray, List]):
    """
    Set a value in the data dictionary of the Detections object.

    Args:
        key (str): The key in the data dictionary to set.
        value (Union[np.ndarray, List]): The value to set for the key.

    Example:
        ```python
        import cv2
        import supervision as sv
        from ultralytics import YOLO

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = YOLO('yolov8s.pt')

        result = model(image)[0]
        detections = sv.Detections.from_ultralytics(result)

        detections['names'] = [
             model.model.names[class_id]
             for class_id
             in detections.class_id
         ]
        ```
    """
    if not isinstance(value, (np.ndarray, list)):
        raise TypeError("Value must be a np.ndarray or a list")

    if isinstance(value, list):
        value = np.array(value)

    self.data[key] = value

empty() classmethod

Create an empty Detections object with no bounding boxes, confidences, or class IDs.

Returns:

Type	Description
Detections	An empty Detections object.

Example

from supervision import Detections

empty_detections = Detections.empty()

Source code in supervision/detection/core.py

@classmethod
def empty(cls) -> Detections:
    """
    Create an empty Detections object with no bounding boxes,
        confidences, or class IDs.

    Returns:
        (Detections): An empty Detections object.

    Example:
        ```python
        from supervision import Detections

        empty_detections = Detections.empty()
        ```
    """
    return cls(
        xyxy=np.empty((0, 4), dtype=np.float32),
        confidence=np.array([], dtype=np.float32),
        class_id=np.array([], dtype=int),
    )

from_azure_analyze_image(azure_result, class_map=None) classmethod

Creates a Detections instance from Azure Image Analysis 4.0.

Parameters:

Name	Type	Description	Default
azure_result	dict	The result from Azure Image Analysis. It should contain detected objects and their bounding box coordinates.	required
class_map	Optional[Dict[int, str]]	A mapping ofclass IDs (int) to class names (str). If None, a new mapping is created dynamically.	None

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import requests
import supervision as sv

image = open(input, "rb").read()

endpoint = "https://.cognitiveservices.azure.com/"
subscription_key = ""

headers = {
    "Content-Type": "application/octet-stream",
    "Ocp-Apim-Subscription-Key": subscription_key
 }

response = requests.post(endpoint,
    headers=self.headers,
    data=image
 ).json()

detections = sv.Detections.from_azure_analyze_image(response)

Source code in supervision/detection/core.py

@classmethod
def from_azure_analyze_image(
    cls, azure_result: dict, class_map: Optional[Dict[int, str]] = None
) -> Detections:
    """
    Creates a Detections instance from [Azure Image Analysis 4.0](
    https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/
    concept-object-detection-40).

    Args:
        azure_result (dict): The result from Azure Image Analysis. It should
            contain detected objects and their bounding box coordinates.
        class_map (Optional[Dict[int, str]]): A mapping ofclass IDs (int) to class
            names (str). If None, a new mapping is created dynamically.

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import requests
        import supervision as sv

        image = open(input, "rb").read()

        endpoint = "https://.cognitiveservices.azure.com/"
        subscription_key = ""

        headers = {
            "Content-Type": "application/octet-stream",
            "Ocp-Apim-Subscription-Key": subscription_key
         }

        response = requests.post(endpoint,
            headers=self.headers,
            data=image
         ).json()

        detections = sv.Detections.from_azure_analyze_image(response)
        ```
    """
    if "error" in azure_result:
        raise ValueError(
            f'Azure API returned an error {azure_result["error"]["message"]}'
        )

    xyxy, confidences, class_ids = [], [], []

    is_dynamic_mapping = class_map is None
    if is_dynamic_mapping:
        class_map = {}

    class_map = {value: key for key, value in class_map.items()}

    for detection in azure_result["objectsResult"]["values"]:
        bbox = detection["boundingBox"]

        tags = detection["tags"]

        x0 = bbox["x"]
        y0 = bbox["y"]
        x1 = x0 + bbox["w"]
        y1 = y0 + bbox["h"]

        for tag in tags:
            confidence = tag["confidence"]
            class_name = tag["name"]
            class_id = class_map.get(class_name, None)

            if is_dynamic_mapping and class_id is None:
                class_id = len(class_map)
                class_map[class_name] = class_id

            if class_id is not None:
                xyxy.append([x0, y0, x1, y1])
                confidences.append(confidence)
                class_ids.append(class_id)

    if len(xyxy) == 0:
        return Detections.empty()

    return cls(
        xyxy=np.array(xyxy),
        class_id=np.array(class_ids),
        confidence=np.array(confidences),
    )

from_deepsparse(deepsparse_results) classmethod

Creates a Detections instance from a DeepSparse inference result.

Parameters:

Name	Type	Description	Default
deepsparse_results	YOLOOutput	The output Results instance from DeepSparse.	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import supervision as sv
from deepsparse import Pipeline

yolo_pipeline = Pipeline.create(
    task="yolo",
    model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
 )
result = yolo_pipeline(<SOURCE IMAGE PATH>)
detections = sv.Detections.from_deepsparse(result)

Source code in supervision/detection/core.py

@classmethod
def from_deepsparse(cls, deepsparse_results) -> Detections:
    """
    Creates a Detections instance from a
    [DeepSparse](https://github.com/neuralmagic/deepsparse)
    inference result.

    Args:
        deepsparse_results (deepsparse.yolo.schemas.YOLOOutput):
            The output Results instance from DeepSparse.

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import supervision as sv
        from deepsparse import Pipeline

        yolo_pipeline = Pipeline.create(
            task="yolo",
            model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
         )
        result = yolo_pipeline(<SOURCE IMAGE PATH>)
        detections = sv.Detections.from_deepsparse(result)
        ```
    """  # noqa: E501 // docs

    if np.asarray(deepsparse_results.boxes[0]).shape[0] == 0:
        return cls.empty()

    return cls(
        xyxy=np.array(deepsparse_results.boxes[0]),
        confidence=np.array(deepsparse_results.scores[0]),
        class_id=np.array(deepsparse_results.labels[0]).astype(float).astype(int),
    )

from_detectron2(detectron2_results) classmethod

Create a Detections object from the Detectron2 inference result.

Parameters:

Name	Type	Description	Default
detectron2_results		The output of a Detectron2 model containing instances with prediction data.	required

Returns:

Type	Description
Detections	A Detections object containing the bounding boxes, class IDs, and confidences of the predictions.

Example

import cv2
import supervision as sv
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg


image = cv2.imread(<SOURCE_IMAGE_PATH>)
cfg = get_cfg()
cfg.merge_from_file(<CONFIG_PATH>)
cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
predictor = DefaultPredictor(cfg)

result = predictor(image)
detections = sv.Detections.from_detectron2(result)

Source code in supervision/detection/core.py

@classmethod
def from_detectron2(cls, detectron2_results) -> Detections:
    """
    Create a Detections object from the
    [Detectron2](https://github.com/facebookresearch/detectron2) inference result.

    Args:
        detectron2_results: The output of a
            Detectron2 model containing instances with prediction data.

    Returns:
        (Detections): A Detections object containing the bounding boxes,
            class IDs, and confidences of the predictions.

    Example:
        ```python
        import cv2
        import supervision as sv
        from detectron2.engine import DefaultPredictor
        from detectron2.config import get_cfg


        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        cfg = get_cfg()
        cfg.merge_from_file(<CONFIG_PATH>)
        cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
        predictor = DefaultPredictor(cfg)

        result = predictor(image)
        detections = sv.Detections.from_detectron2(result)
        ```
    """

    return cls(
        xyxy=detectron2_results["instances"].pred_boxes.tensor.cpu().numpy(),
        confidence=detectron2_results["instances"].scores.cpu().numpy(),
        mask=detectron2_results["instances"].pred_masks.cpu().numpy()
        if hasattr(detectron2_results["instances"], "pred_masks")
        else None,
        class_id=detectron2_results["instances"]
        .pred_classes.cpu()
        .numpy()
        .astype(int),
    )

from_inference(roboflow_result) classmethod

Create a sv.Detections object from the Roboflow API inference result or the Inference package results. This method extracts bounding boxes, class IDs, confidences, and class names from the Roboflow API result and encapsulates them into a Detections object.

Parameters:

Name	Type	Description	Default
roboflow_result	(dict, any)	The result from the Roboflow API or Inference package containing predictions.	required

Returns:

Type	Description
Detections	A Detections object containing the bounding boxes, class IDs, and confidences of the predictions.

Example

import cv2
import supervision as sv
from inference import get_model

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id="yolov8s-640")

result = model.infer(image)[0]
detections = sv.Detections.from_inference(result)

Tip

Class names values can be accessed using detections["class_name"].

Source code in supervision/detection/core.py

@classmethod
def from_inference(cls, roboflow_result: Union[dict, Any]) -> Detections:
    """
    Create a `sv.Detections` object from the [Roboflow](https://roboflow.com/)
    API inference result or the [Inference](https://inference.roboflow.com/)
    package results. This method extracts bounding boxes, class IDs,
    confidences, and class names from the Roboflow API result and encapsulates
    them into a Detections object.

    Args:
        roboflow_result (dict, any): The result from the
            Roboflow API or Inference package containing predictions.

    Returns:
        (Detections): A Detections object containing the bounding boxes, class IDs,
            and confidences of the predictions.

    Example:
        ```python
        import cv2
        import supervision as sv
        from inference import get_model

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = get_model(model_id="yolov8s-640")

        result = model.infer(image)[0]
        detections = sv.Detections.from_inference(result)
        ```

    !!! tip

        Class names values can be accessed using `detections["class_name"]`.
    """
    with suppress(AttributeError):
        roboflow_result = roboflow_result.dict(exclude_none=True, by_alias=True)
    xyxy, confidence, class_id, masks, trackers, data = process_roboflow_result(
        roboflow_result=roboflow_result
    )

    if np.asarray(xyxy).shape[0] == 0:
        empty_detection = cls.empty()
        empty_detection.data = {CLASS_NAME_DATA_FIELD: np.empty(0)}
        return empty_detection

    return cls(
        xyxy=xyxy,
        confidence=confidence,
        class_id=class_id,
        mask=masks,
        tracker_id=trackers,
        data=data,
    )

from_lmm(lmm, result, **kwargs) classmethod

Creates a Detections object from the given result string based on the specified Large Multimodal Model (LMM).

Parameters:

Name	Type	Description	Default
lmm	Union[LMM, str]	The type of LMM (Large Multimodal Model) to use.	required
result	str	The result string containing the detection data.	required
**kwargs		Additional keyword arguments required by the specified LMM.	{}

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Raises:

Type	Description
ValueError	If the LMM is invalid, required arguments are missing, or disallowed arguments are provided.
ValueError	If the specified LMM is not supported.

Examples:

import supervision as sv

paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
detections = sv.Detections.from_lmm(
    sv.LMM.PALIGEMMA,
    paligemma_result,
    resolution_wh=(1000, 1000),
    classes=['cat', 'dog']
)
detections.xyxy
# array([[250., 250., 750., 750.]])

detections.class_id
# array([0])

Source code in supervision/detection/core.py

@classmethod
def from_lmm(
    cls, lmm: Union[LMM, str], result: Union[str, dict], **kwargs
) -> Detections:
    """
    Creates a Detections object from the given result string based on the specified
    Large Multimodal Model (LMM).

    Args:
        lmm (Union[LMM, str]): The type of LMM (Large Multimodal Model) to use.
        result (str): The result string containing the detection data.
        **kwargs: Additional keyword arguments required by the specified LMM.

    Returns:
        Detections: A new Detections object.

    Raises:
        ValueError: If the LMM is invalid, required arguments are missing, or
            disallowed arguments are provided.
        ValueError: If the specified LMM is not supported.

    Examples:
        ```python
        import supervision as sv

        paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
        detections = sv.Detections.from_lmm(
            sv.LMM.PALIGEMMA,
            paligemma_result,
            resolution_wh=(1000, 1000),
            classes=['cat', 'dog']
        )
        detections.xyxy
        # array([[250., 250., 750., 750.]])

        detections.class_id
        # array([0])
        ```
    """
    lmm = validate_lmm_parameters(lmm, result, kwargs)

    if lmm == LMM.PALIGEMMA:
        assert isinstance(result, str)
        xyxy, class_id, class_name = from_paligemma(result, **kwargs)
        data = {CLASS_NAME_DATA_FIELD: class_name}
        return cls(xyxy=xyxy, class_id=class_id, data=data)

    if lmm == LMM.FLORENCE_2:
        assert isinstance(result, dict)
        xyxy, labels, mask, xyxyxyxy = from_florence_2(result, **kwargs)
        if len(xyxy) == 0:
            return cls.empty()

        data = {}
        if labels is not None:
            data[CLASS_NAME_DATA_FIELD] = labels
        if xyxyxyxy is not None:
            data[ORIENTED_BOX_COORDINATES] = xyxyxyxy

        return cls(xyxy=xyxy, mask=mask, data=data)

    raise ValueError(f"Unsupported LMM: {lmm}")

from_mmdetection(mmdet_results) classmethod

Creates a Detections instance from a mmdetection and mmyolo inference result.

Parameters:

Name	Type	Description	Default
mmdet_results	DetDataSample	The output Results instance from MMDetection.	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import cv2
import supervision as sv
from mmdet.apis import init_detector, inference_detector

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = init_detector(<CONFIG_PATH>, <WEIGHTS_PATH>, device=<DEVICE>)

result = inference_detector(model, image)
detections = sv.Detections.from_mmdetection(result)

Source code in supervision/detection/core.py

@classmethod
def from_mmdetection(cls, mmdet_results) -> Detections:
    """
    Creates a Detections instance from a
    [mmdetection](https://github.com/open-mmlab/mmdetection) and
    [mmyolo](https://github.com/open-mmlab/mmyolo) inference result.

    Args:
        mmdet_results (mmdet.structures.DetDataSample):
            The output Results instance from MMDetection.

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import cv2
        import supervision as sv
        from mmdet.apis import init_detector, inference_detector

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = init_detector(<CONFIG_PATH>, <WEIGHTS_PATH>, device=<DEVICE>)

        result = inference_detector(model, image)
        detections = sv.Detections.from_mmdetection(result)
        ```
    """  # noqa: E501 // docs

    return cls(
        xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
        confidence=mmdet_results.pred_instances.scores.cpu().numpy(),
        class_id=mmdet_results.pred_instances.labels.cpu().numpy().astype(int),
        mask=mmdet_results.pred_instances.masks.cpu().numpy()
        if "masks" in mmdet_results.pred_instances
        else None,
    )

from_paddledet(paddledet_result) classmethod

Creates a Detections instance from PaddleDetection inference result.

Parameters:

Name	Type	Description	Default
paddledet_result	List[dict]	The output Results instance from PaddleDet	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import supervision as sv
import paddle
from ppdet.engine import Trainer
from ppdet.core.workspace import load_config

weights = ()
config = ()

cfg = load_config(config)
trainer = Trainer(cfg, mode='test')
trainer.load_weights(weights)

paddledet_result = trainer.predict([images])[0]

detections = sv.Detections.from_paddledet(paddledet_result)

Source code in supervision/detection/core.py

@classmethod
def from_paddledet(cls, paddledet_result) -> Detections:
    """
    Creates a Detections instance from
        [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)
        inference result.

    Args:
        paddledet_result (List[dict]): The output Results instance from PaddleDet

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import supervision as sv
        import paddle
        from ppdet.engine import Trainer
        from ppdet.core.workspace import load_config

        weights = ()
        config = ()

        cfg = load_config(config)
        trainer = Trainer(cfg, mode='test')
        trainer.load_weights(weights)

        paddledet_result = trainer.predict([images])[0]

        detections = sv.Detections.from_paddledet(paddledet_result)
        ```
    """

    if np.asarray(paddledet_result["bbox"][:, 2:6]).shape[0] == 0:
        return cls.empty()

    return cls(
        xyxy=paddledet_result["bbox"][:, 2:6],
        confidence=paddledet_result["bbox"][:, 1],
        class_id=paddledet_result["bbox"][:, 0].astype(int),
    )

from_sam(sam_result) classmethod

Creates a Detections instance from Segment Anything Model inference result.

Parameters:

Name	Type	Description	Default
sam_result	List[dict]	The output Results instance from SAM	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import supervision as sv
from segment_anything import (
    sam_model_registry,
    SamAutomaticMaskGenerator
 )

sam_model_reg = sam_model_registry[MODEL_TYPE]
sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
mask_generator = SamAutomaticMaskGenerator(sam)
sam_result = mask_generator.generate(IMAGE)
detections = sv.Detections.from_sam(sam_result=sam_result)

Source code in supervision/detection/core.py

@classmethod
def from_sam(cls, sam_result: List[dict]) -> Detections:
    """
    Creates a Detections instance from
    [Segment Anything Model](https://github.com/facebookresearch/segment-anything)
    inference result.

    Args:
        sam_result (List[dict]): The output Results instance from SAM

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import supervision as sv
        from segment_anything import (
            sam_model_registry,
            SamAutomaticMaskGenerator
         )

        sam_model_reg = sam_model_registry[MODEL_TYPE]
        sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
        mask_generator = SamAutomaticMaskGenerator(sam)
        sam_result = mask_generator.generate(IMAGE)
        detections = sv.Detections.from_sam(sam_result=sam_result)
        ```
    """

    sorted_generated_masks = sorted(
        sam_result, key=lambda x: x["area"], reverse=True
    )

    xywh = np.array([mask["bbox"] for mask in sorted_generated_masks])
    mask = np.array([mask["segmentation"] for mask in sorted_generated_masks])

    if np.asarray(xywh).shape[0] == 0:
        return cls.empty()

    xyxy = xywh_to_xyxy(xywh=xywh)
    return cls(xyxy=xyxy, mask=mask)

from_tensorflow(tensorflow_results, resolution_wh) classmethod

Creates a Detections instance from a Tensorflow Hub inference result.

Parameters:

Name	Type	Description	Default
tensorflow_results	dict	The output results from Tensorflow Hub.	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import cv2

module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
model = hub.load(module_handle)
img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
result = model(img)
detections = sv.Detections.from_tensorflow(result)

Source code in supervision/detection/core.py

@classmethod
def from_tensorflow(
    cls, tensorflow_results: dict, resolution_wh: tuple
) -> Detections:
    """
    Creates a Detections instance from a
    [Tensorflow Hub](https://www.tensorflow.org/hub/tutorials/tf2_object_detection)
    inference result.

    Args:
        tensorflow_results (dict):
            The output results from Tensorflow Hub.

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import tensorflow as tf
        import tensorflow_hub as hub
        import numpy as np
        import cv2

        module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
        model = hub.load(module_handle)
        img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
        result = model(img)
        detections = sv.Detections.from_tensorflow(result)
        ```
    """  # noqa: E501 // docs

    boxes = tensorflow_results["detection_boxes"][0].numpy()
    boxes[:, [0, 2]] *= resolution_wh[0]
    boxes[:, [1, 3]] *= resolution_wh[1]
    boxes = boxes[:, [1, 0, 3, 2]]
    return cls(
        xyxy=boxes,
        confidence=tensorflow_results["detection_scores"][0].numpy(),
        class_id=tensorflow_results["detection_classes"][0].numpy().astype(int),
    )

from_transformers(transformers_results, id2label=None) classmethod

Creates a Detections instance from object detection or segmentation Transformer inference result.

Parameters:

Name	Type	Description	Default
transformers_results	dict	The output of Transformers model inference. A dictionary containing the scores, labels, boxes and masks keys.	required
id2label	Optional[Dict[int, str]]	A dictionary mapping class IDs to class names. If provided, the resulting Detections object will contain class_name data field with the class names.	None

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import torch
import supervision as sv
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

image = Image.open(<SOURCE_IMAGE_PATH>)
inputs = processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
    outputs=outputs, target_sizes=target_size)[0]

detections = sv.Detections.from_transformers(
    transformers_results=results,
    id2label=model.config.id2label
)

Tip

Class names values can be accessed using detections["class_name"].

Source code in supervision/detection/core.py

@classmethod
def from_transformers(
    cls, transformers_results: dict, id2label: Optional[Dict[int, str]] = None
) -> Detections:
    """
    Creates a Detections instance from object detection or segmentation
    [Transformer](https://github.com/huggingface/transformers) inference result.

    Args:
        transformers_results (dict): The output of Transformers model inference. A
            dictionary containing the `scores`, `labels`, `boxes` and `masks` keys.
        id2label (Optional[Dict[int, str]]): A dictionary mapping class IDs to
            class names. If provided, the resulting Detections object will contain
            `class_name` data field with the class names.

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import torch
        import supervision as sv
        from PIL import Image
        from transformers import DetrImageProcessor, DetrForObjectDetection

        processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
        model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

        image = Image.open(<SOURCE_IMAGE_PATH>)
        inputs = processor(images=image, return_tensors="pt")

        with torch.no_grad():
            outputs = model(**inputs)

        width, height = image.size
        target_size = torch.tensor([[height, width]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]

        detections = sv.Detections.from_transformers(
            transformers_results=results,
            id2label=model.config.id2label
        )
        ```

    !!! tip

        Class names values can be accessed using `detections["class_name"]`.
    """  # noqa: E501 // docs

    class_ids = transformers_results["labels"].cpu().detach().numpy().astype(int)
    data = {}
    if id2label is not None:
        class_names = np.array([id2label[class_id] for class_id in class_ids])
        data[CLASS_NAME_DATA_FIELD] = class_names
    if "boxes" in transformers_results:
        return cls(
            xyxy=transformers_results["boxes"].cpu().detach().numpy(),
            confidence=transformers_results["scores"].cpu().detach().numpy(),
            class_id=class_ids,
            data=data,
        )
    elif "masks" in transformers_results:
        masks = transformers_results["masks"].cpu().detach().numpy().astype(bool)
        return cls(
            xyxy=mask_to_xyxy(masks),
            mask=masks,
            confidence=transformers_results["scores"].cpu().detach().numpy(),
            class_id=class_ids,
            data=data,
        )
    else:
        raise NotImplementedError(
            "Only object detection and semantic segmentation results are supported."
        )

from_ultralytics(ultralytics_results) classmethod

Creates a sv.Detections instance from a YOLOv8 inference result.

Note

from_ultralytics is compatible with detection, segmentation, and OBB models.

Parameters:

Name	Type	Description	Default
ultralytics_results	Results	The output Results instance from Ultralytics	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import cv2
import supervision as sv
from ultralytics import YOLO

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s.pt')
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)

Tip

Class names values can be accessed using detections["class_name"].

Source code in supervision/detection/core.py

@classmethod
def from_ultralytics(cls, ultralytics_results) -> Detections:
    """
    Creates a `sv.Detections` instance from a
    [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.

    !!! Note

        `from_ultralytics` is compatible with
        [detection](https://docs.ultralytics.com/tasks/detect/),
        [segmentation](https://docs.ultralytics.com/tasks/segment/), and
        [OBB](https://docs.ultralytics.com/tasks/obb/) models.

    Args:
        ultralytics_results (ultralytics.yolo.engine.results.Results):
            The output Results instance from Ultralytics

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import cv2
        import supervision as sv
        from ultralytics import YOLO

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = YOLO('yolov8s.pt')
        results = model(image)[0]
        detections = sv.Detections.from_ultralytics(results)
        ```

    !!! tip

        Class names values can be accessed using `detections["class_name"]`.
    """  # noqa: E501 // docs

    if hasattr(ultralytics_results, "obb") and ultralytics_results.obb is not None:
        class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
        class_names = np.array([ultralytics_results.names[i] for i in class_id])
        oriented_box_coordinates = ultralytics_results.obb.xyxyxyxy.cpu().numpy()
        return cls(
            xyxy=ultralytics_results.obb.xyxy.cpu().numpy(),
            confidence=ultralytics_results.obb.conf.cpu().numpy(),
            class_id=class_id,
            tracker_id=ultralytics_results.obb.id.int().cpu().numpy()
            if ultralytics_results.obb.id is not None
            else None,
            data={
                ORIENTED_BOX_COORDINATES: oriented_box_coordinates,
                CLASS_NAME_DATA_FIELD: class_names,
            },
        )

    class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
    class_names = np.array([ultralytics_results.names[i] for i in class_id])
    return cls(
        xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
        confidence=ultralytics_results.boxes.conf.cpu().numpy(),
        class_id=class_id,
        mask=extract_ultralytics_masks(ultralytics_results),
        tracker_id=ultralytics_results.boxes.id.int().cpu().numpy()
        if ultralytics_results.boxes.id is not None
        else None,
        data={CLASS_NAME_DATA_FIELD: class_names},
    )

from_yolo_nas(yolo_nas_results) classmethod

Creates a Detections instance from a YOLO-NAS inference result.

Parameters:

Name	Type	Description	Default
yolo_nas_results	ImageDetectionPrediction	The output Results instance from YOLO-NAS ImageDetectionPrediction is coming from 'super_gradients.training.models.prediction_results'	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import cv2
from super_gradients.training import models
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = models.get('yolo_nas_l', pretrained_weights="coco")

result = list(model.predict(image, conf=0.35))[0]
detections = sv.Detections.from_yolo_nas(result)

Source code in supervision/detection/core.py

@classmethod
def from_yolo_nas(cls, yolo_nas_results) -> Detections:
    """
    Creates a Detections instance from a
    [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md)
    inference result.

    Args:
        yolo_nas_results (ImageDetectionPrediction):
            The output Results instance from YOLO-NAS
            ImageDetectionPrediction is coming from
            'super_gradients.training.models.prediction_results'

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import cv2
        from super_gradients.training import models
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = models.get('yolo_nas_l', pretrained_weights="coco")

        result = list(model.predict(image, conf=0.35))[0]
        detections = sv.Detections.from_yolo_nas(result)
        ```
    """
    if np.asarray(yolo_nas_results.prediction.bboxes_xyxy).shape[0] == 0:
        return cls.empty()

    return cls(
        xyxy=yolo_nas_results.prediction.bboxes_xyxy,
        confidence=yolo_nas_results.prediction.confidence,
        class_id=yolo_nas_results.prediction.labels.astype(int),
    )

from_yolov5(yolov5_results) classmethod

Creates a Detections instance from a YOLOv5 inference result.

Parameters:

Name	Type	Description	Default
yolov5_results	Detections	The output Detections instance from YOLOv5	required

Returns:

Name	Type	Description
Detections	Detections	A new Detections object.

Example

import cv2
import torch
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
result = model(image)
detections = sv.Detections.from_yolov5(result)

Source code in supervision/detection/core.py

@classmethod
def from_yolov5(cls, yolov5_results) -> Detections:
    """
    Creates a Detections instance from a
    [YOLOv5](https://github.com/ultralytics/yolov5) inference result.

    Args:
        yolov5_results (yolov5.models.common.Detections):
            The output Detections instance from YOLOv5

    Returns:
        Detections: A new Detections object.

    Example:
        ```python
        import cv2
        import torch
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
        result = model(image)
        detections = sv.Detections.from_yolov5(result)
        ```
    """
    yolov5_detections_predictions = yolov5_results.pred[0].cpu().cpu().numpy()

    return cls(
        xyxy=yolov5_detections_predictions[:, :4],
        confidence=yolov5_detections_predictions[:, 4],
        class_id=yolov5_detections_predictions[:, 5].astype(int),
    )

get_anchors_coordinates(anchor)

Calculates and returns the coordinates of a specific anchor point within the bounding boxes defined by the xyxy attribute. The anchor point can be any of the predefined positions in the Position enum, such as CENTER, CENTER_LEFT, BOTTOM_RIGHT, etc.

Parameters:

Name	Type	Description	Default
anchor	Position	An enum specifying the position of the anchor point within the bounding box. Supported positions are defined in the Position enum.	required

Returns:

Type	Description
ndarray	np.ndarray: An array of shape (n, 2), where n is the number of bounding boxes. Each row contains the [x, y] coordinates of the specified anchor point for the corresponding bounding box.

Raises:

Type	Description
ValueError	If the provided anchor is not supported.

Source code in supervision/detection/core.py

def get_anchors_coordinates(self, anchor: Position) -> np.ndarray:
    """
    Calculates and returns the coordinates of a specific anchor point
    within the bounding boxes defined by the `xyxy` attribute. The anchor
    point can be any of the predefined positions in the `Position` enum,
    such as `CENTER`, `CENTER_LEFT`, `BOTTOM_RIGHT`, etc.

    Args:
        anchor (Position): An enum specifying the position of the anchor point
            within the bounding box. Supported positions are defined in the
            `Position` enum.

    Returns:
        np.ndarray: An array of shape `(n, 2)`, where `n` is the number of bounding
            boxes. Each row contains the `[x, y]` coordinates of the specified
            anchor point for the corresponding bounding box.

    Raises:
        ValueError: If the provided `anchor` is not supported.
    """
    if anchor == Position.CENTER:
        return np.array(
            [
                (self.xyxy[:, 0] + self.xyxy[:, 2]) / 2,
                (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
            ]
        ).transpose()
    elif anchor == Position.CENTER_OF_MASS:
        if self.mask is None:
            raise ValueError(
                "Cannot use `Position.CENTER_OF_MASS` without a detection mask."
            )
        return calculate_masks_centroids(masks=self.mask)
    elif anchor == Position.CENTER_LEFT:
        return np.array(
            [
                self.xyxy[:, 0],
                (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
            ]
        ).transpose()
    elif anchor == Position.CENTER_RIGHT:
        return np.array(
            [
                self.xyxy[:, 2],
                (self.xyxy[:, 1] + self.xyxy[:, 3]) / 2,
            ]
        ).transpose()
    elif anchor == Position.BOTTOM_CENTER:
        return np.array(
            [(self.xyxy[:, 0] + self.xyxy[:, 2]) / 2, self.xyxy[:, 3]]
        ).transpose()
    elif anchor == Position.BOTTOM_LEFT:
        return np.array([self.xyxy[:, 0], self.xyxy[:, 3]]).transpose()
    elif anchor == Position.BOTTOM_RIGHT:
        return np.array([self.xyxy[:, 2], self.xyxy[:, 3]]).transpose()
    elif anchor == Position.TOP_CENTER:
        return np.array(
            [(self.xyxy[:, 0] + self.xyxy[:, 2]) / 2, self.xyxy[:, 1]]
        ).transpose()
    elif anchor == Position.TOP_LEFT:
        return np.array([self.xyxy[:, 0], self.xyxy[:, 1]]).transpose()
    elif anchor == Position.TOP_RIGHT:
        return np.array([self.xyxy[:, 2], self.xyxy[:, 1]]).transpose()

    raise ValueError(f"{anchor} is not supported.")

is_empty()

Returns True if the Detections object is considered empty.

Source code in supervision/detection/core.py

def is_empty(self) -> bool:
    """
    Returns `True` if the `Detections` object is considered empty.
    """
    empty_detections = Detections.empty()
    empty_detections.data = self.data
    return self == empty_detections

merge(detections_list) classmethod

Merge a list of Detections objects into a single Detections object.

This method takes a list of Detections objects and combines their respective fields (xyxy, mask, confidence, class_id, and tracker_id) into a single Detections object.

For example, if merging Detections with 3 and 4 detected objects, this method will return a Detections with 7 objects (7 entries in xyxy, mask, etc).

Note

When merging, empty Detections objects are ignored.

Parameters:

Name	Type	Description	Default
detections_list	List[Detections]	A list of Detections objects to merge.	required

Returns:

Type	Description
Detections	A single Detections object containing the merged data from the input list.

Example

import numpy as np
import supervision as sv

detections_1 = sv.Detections(
    xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
    class_id=np.array([1, 2]),
    data={'feature_vector': np.array([0.1, 0.2)])}
 )

detections_2 = sv.Detections(
    xyxy=np.array([[30, 30, 120, 120]]),
    class_id=np.array([1]),
    data={'feature_vector': [np.array([0.3])]}
 )

merged_detections = Detections.merge([detections_1, detections_2])

merged_detections.xyxy
array([[ 15,  15, 100, 100],
       [200, 200, 300, 300],
       [ 30,  30, 120, 120]])

merged_detections.class_id
array([1, 2, 1])

merged_detections.data['feature_vector']
array([0.1, 0.2, 0.3])

Source code in supervision/detection/core.py

@classmethod
def merge(cls, detections_list: List[Detections]) -> Detections:
    """
    Merge a list of Detections objects into a single Detections object.

    This method takes a list of Detections objects and combines their
    respective fields (`xyxy`, `mask`, `confidence`, `class_id`, and `tracker_id`)
    into a single Detections object.

    For example, if merging Detections with 3 and 4 detected objects, this method
    will return a Detections with 7 objects (7 entries in `xyxy`, `mask`, etc).

    !!! Note

        When merging, empty `Detections` objects are ignored.

    Args:
        detections_list (List[Detections]): A list of Detections objects to merge.

    Returns:
        (Detections): A single Detections object containing
            the merged data from the input list.

    Example:
        ```python
        import numpy as np
        import supervision as sv

        detections_1 = sv.Detections(
            xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
            class_id=np.array([1, 2]),
            data={'feature_vector': np.array([0.1, 0.2)])}
         )

        detections_2 = sv.Detections(
            xyxy=np.array([[30, 30, 120, 120]]),
            class_id=np.array([1]),
            data={'feature_vector': [np.array([0.3])]}
         )

        merged_detections = Detections.merge([detections_1, detections_2])

        merged_detections.xyxy
        array([[ 15,  15, 100, 100],
               [200, 200, 300, 300],
               [ 30,  30, 120, 120]])

        merged_detections.class_id
        array([1, 2, 1])

        merged_detections.data['feature_vector']
        array([0.1, 0.2, 0.3])
        ```
    """
    detections_list = [
        detections for detections in detections_list if not detections.is_empty()
    ]

    if len(detections_list) == 0:
        return Detections.empty()

    for detections in detections_list:
        validate_detections_fields(
            xyxy=detections.xyxy,
            mask=detections.mask,
            confidence=detections.confidence,
            class_id=detections.class_id,
            tracker_id=detections.tracker_id,
            data=detections.data,
        )

    xyxy = np.vstack([d.xyxy for d in detections_list])

    def stack_or_none(name: str):
        if all(d.__getattribute__(name) is None for d in detections_list):
            return None
        if any(d.__getattribute__(name) is None for d in detections_list):
            raise ValueError(f"All or none of the '{name}' fields must be None")
        return (
            np.vstack([d.__getattribute__(name) for d in detections_list])
            if name == "mask"
            else np.hstack([d.__getattribute__(name) for d in detections_list])
        )

    mask = stack_or_none("mask")
    confidence = stack_or_none("confidence")
    class_id = stack_or_none("class_id")
    tracker_id = stack_or_none("tracker_id")

    data = merge_data([d.data for d in detections_list])

    return cls(
        xyxy=xyxy,
        mask=mask,
        confidence=confidence,
        class_id=class_id,
        tracker_id=tracker_id,
        data=data,
    )

with_nmm(threshold=0.5, class_agnostic=False)

Perform non-maximum merging on the current set of object detections.

Parameters:

Name	Type	Description	Default
threshold	float	The intersection-over-union threshold to use for non-maximum merging. Defaults to 0.5.	0.5
class_agnostic	bool	Whether to perform class-agnostic non-maximum merging. If True, the class_id of each detection will be ignored. Defaults to False.	False

Returns:

Name	Type	Description
Detections	Detections	A new Detections object containing the subset of detections after non-maximum merging.

Raises:

Type	Description
AssertionError	If confidence is None or class_id is None and class_agnostic is False.

Source code in supervision/detection/core.py

def with_nmm(
    self, threshold: float = 0.5, class_agnostic: bool = False
) -> Detections:
    """
    Perform non-maximum merging on the current set of object detections.

    Args:
        threshold (float, optional): The intersection-over-union threshold
            to use for non-maximum merging. Defaults to 0.5.
        class_agnostic (bool, optional): Whether to perform class-agnostic
            non-maximum merging. If True, the class_id of each detection
            will be ignored. Defaults to False.

    Returns:
        Detections: A new Detections object containing the subset of detections
            after non-maximum merging.

    Raises:
        AssertionError: If `confidence` is None or `class_id` is None and
            class_agnostic is False.

    ![non-max-merging](https://media.roboflow.com/supervision-docs/non-max-merging.png){ align=center width="800" }
    """  # noqa: E501 // docs
    if len(self) == 0:
        return self

    assert (
        self.confidence is not None
    ), "Detections confidence must be given for NMM to be executed."

    if class_agnostic:
        predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
    else:
        assert self.class_id is not None, (
            "Detections class_id must be given for NMM to be executed. If you"
            " intended to perform class agnostic NMM set class_agnostic=True."
        )
        predictions = np.hstack(
            (
                self.xyxy,
                self.confidence.reshape(-1, 1),
                self.class_id.reshape(-1, 1),
            )
        )

    merge_groups = box_non_max_merge(
        predictions=predictions, iou_threshold=threshold
    )

    result = []
    for merge_group in merge_groups:
        unmerged_detections = [self[i] for i in merge_group]
        merged_detections = merge_inner_detections_objects(
            unmerged_detections, threshold
        )
        result.append(merged_detections)

    return Detections.merge(result)

with_nms(threshold=0.5, class_agnostic=False)

Performs non-max suppression on detection set. If the detections result from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.

Parameters:

Name	Type	Description	Default
threshold	float	The intersection-over-union threshold to use for non-maximum suppression. I'm the lower the value the more restrictive the NMS becomes. Defaults to 0.5.	0.5
class_agnostic	bool	Whether to perform class-agnostic non-maximum suppression. If True, the class_id of each detection will be ignored. Defaults to False.	False

Returns:

Name	Type	Description
Detections	Detections	A new Detections object containing the subset of detections after non-maximum suppression.

Raises:

Type	Description
AssertionError	If confidence is None and class_agnostic is False. If class_id is None and class_agnostic is False.

Source code in supervision/detection/core.py

def with_nms(
    self, threshold: float = 0.5, class_agnostic: bool = False
) -> Detections:
    """
    Performs non-max suppression on detection set. If the detections result
    from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.

    Args:
        threshold (float, optional): The intersection-over-union threshold
            to use for non-maximum suppression. I'm the lower the value the more
            restrictive the NMS becomes. Defaults to 0.5.
        class_agnostic (bool, optional): Whether to perform class-agnostic
            non-maximum suppression. If True, the class_id of each detection
            will be ignored. Defaults to False.

    Returns:
        Detections: A new Detections object containing the subset of detections
            after non-maximum suppression.

    Raises:
        AssertionError: If `confidence` is None and class_agnostic is False.
            If `class_id` is None and class_agnostic is False.
    """
    if len(self) == 0:
        return self

    assert (
        self.confidence is not None
    ), "Detections confidence must be given for NMS to be executed."

    if class_agnostic:
        predictions = np.hstack((self.xyxy, self.confidence.reshape(-1, 1)))
    else:
        assert self.class_id is not None, (
            "Detections class_id must be given for NMS to be executed. If you"
            " intended to perform class agnostic NMS set class_agnostic=True."
        )
        predictions = np.hstack(
            (
                self.xyxy,
                self.confidence.reshape(-1, 1),
                self.class_id.reshape(-1, 1),
            )
        )

    if self.mask is not None:
        indices = mask_non_max_suppression(
            predictions=predictions, masks=self.mask, iou_threshold=threshold
        )
    else:
        indices = box_non_max_suppression(
            predictions=predictions, iou_threshold=threshold
        )

    return self[indices]

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