Predictions for complex multi-image scenario. 15 images with varying detection quality: - True positives, false positives, false negativ

Ground truth for complex multi-image scenario. 15 images with varying object counts and classes. Total: class_0=17, class_1=19 objects.

Calculate the recall metric based on the stored predictions and ground-truth data, at different IoU thresholds. Returns:

Calculate the F1 score metric based on the stored predictions and ground-truth data, at different IoU thresholds. Returns:

Calculate the precision metric based on the stored predictions and ground-truth data, at different IoU thresholds. Returns:

Compute average precision while handling -1 sentinel values.

Deprecated since 0.29.0. Use ``keypoint_confidence`` instead.

Creates tiles mosaic from input images, automating grid placement and converting images to common resolution maintaining aspect ratio. It is

A decorator to mark a function's parameter as deprecated and issue a warning when used. Args: old_parameter: The name of the dep

Draws an image onto a given scene with specified opacity and dimensions. Args: scene: Background image where the new image will be d

Draws a line on a given scene. Args: scene: The scene on which the line will be drawn start: The starting point of the line

Decorates `BaseAnnotator.annotate` implementations, converts scene to an image type used internally by the annotators, converts back when ann

Decorates image processing functions that accept np.ndarray, converting `image` to np.ndarray, converts back when processing is complete.

Decorates image processing functions that accept np.ndarray, converting `image` to PIL image, converts back when processing is complete.

Format a warning the same way as the default formatter, but also include the category name in the output.

Computes and returns the average FPS based on the stored time stamps. Returns: The average FPS. Returns 0.0 if no time s

Creates a Detections instance from [Azure Image Analysis 4.0]( https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/

Creates a Detections instance from a [DeepSparse](https://github.com/neuralmagic/deepsparse) inference result. Args:

Calculate mean average precision based on predicted and ground-truth detections. Args: targets: Detections objects from

Create a Detections object from the [Detectron2](https://github.com/facebookresearch/detectron2) inference result. Args:

Create a `sv.KeyPoints` object from the [Detectron2](https://github.com/facebookresearch/detectron2) inference result. Args:

Create a Detections object from the [EasyOCR](https://github.com/JaidedAI/EasyOCR) result. Results are placed in the `data`

Load data from a multiclass folder structure into a ClassificationDataset. Args: root_directory_path: The path to the da

Create a ColorPalette instance from a list of hex strings. Args: color_hex_list: List of color hex strings. Ret

Create a `sv.KeyPoints` object from the [Roboflow](https://roboflow.com/) API inference result or the [Inference](https://inference.r

!!! deprecated "Deprecated" `Detections.from_lmm` is **deprecated** and will be removed in `supervision-0.31.0`. Plea

Create a ColorPalette instance from a Matplotlib color palette. Args: palette_name: Name of the Matplotlib palette.

Creates a Detections instance from a [mmdetection](https://github.com/open-mmlab/mmdetection) and [mmyolo](https://github.com

Creates a Detections instance from the [ncnn](https://github.com/Tencent/ncnn) inference result. Supports object detection mo

Creates a Detections instance from [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) inference resul

Creates a Dataset instance from PASCAL VOC formatted data. Args: images_directory_path: Path to the directory containing

Creates a Detections instance from a [Tensorflow Hub](https://www.tensorflow.org/hub/tutorials/tf2_object_detection) inferenc

Calculate Mean Average Precision based on predicted and ground-truth detections at different threshold. Args:

Creates a Detections instance from object detection or panoptic, semantic and instance segmentation [Transformer](https://git

Create a `sv.KeyPoints` object from the [Transformers](https://github.com/huggingface/transformers) inference result. Args:

Creates a Classifications instance from a [ultralytics](https://github.com/ultralytics/ultralytics) inference result. Args:

Creates a `sv.KeyPoints` instance from a [YOLOv8](https://github.com/ultralytics/ultralytics) pose inference result. Args:

Create a `sv.KeyPoints` instance from a [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md) pose infer

Generate a semicircle with a given number of points. Parameters: n (int): amount of points in polygon Returns: Pol

Generates a mapping from sequential class indices to original COCO class ids. This function is essential when working with models that expec

Get the size category of an object. Distinguish based on the metric target. Args: data: The object data, shaped (N, ...). me

Create a gradient test image fixture

Convert image to 3-channel grayscale. Luminance channel is broadcast to all three channels for compatibility with color-based drawing helpers

Calculate the magnitude (length) of the vector. Returns: The magnitude of the vector.

Heatmap and Tracking with Supervision. Args: source_weights_path: Path to the source weights file source_video_path: Pa

Traffic Flow Analysis with Inference and ByteTrack. Args: source_video_path: Path to the source video file target_video_path

Traffic Flow Analysis with YOLO and ByteTrack. Args: source_weights_path: Path to the source weights file source_video_path:

Calculating detections dwell time in zones, using video file. Args: zone_configuration_path: Path to the zone configuration JSON fil

Calculating detections dwell time in zones, using RTSP stream. Args: zone_configuration_path: Path to the zone configuration JSON fi

Calculating detections dwell time in zones, using RTSP stream. Args: rtsp_url: Complete RTSP URL for the video stream zone_c

Calculating detections dwell time in zones, using RTSP stream. Args: zone_configuration_path: Path to the zone configuration JSON fi

Calculating detections dwell time in zones, using video file. Args: source_video_path: Path to the source video file zone_co

Calculating detections dwell time in zones, using RTSP stream. Args: zone_configuration_path: Path to the zone configuration JSON fi

Calculating detections dwell time in zones, using RTSP stream. Args: zone_configuration_path: Path to the zone configuration JSON fi

Calculating detections dwell time in zones using an RTSP stream. Args: rtsp_url: Complete RTSP URL for the video stream zone