Struct Metadata

Defined in File Metadata.hpp

Struct Documentation

struct Metadata

Metadata of the parser.

Metadata for the object detection head.

@type classes: list @ivar classes: Names of object classes detected by the model. @type n_classes: int @ivar n_classes: Number of object classes detected by the model. @type iou_threshold: float @ivar iou_threshold: Non-max supression threshold limiting boxes intersection. @type conf_threshold: float @ivar conf_threshold: Confidence score threshold above which a detected object is considered valid. @type max_det: int @ivar max_det: Maximum detections per image. @type anchors: list @ivar anchors: Predefined bounding boxes of different sizes and aspect ratios. The innermost lists are length 2 tuples of box sizes. The middle lists are anchors for each output. The outmost lists go from smallest to largest output.

Metadata for the classification head.

@type classes: list @ivar classes: Names of object classes classified by the model. @type n_classes: int @ivar n_classes: Number of object classes classified by the model. @type is_softmax: bool @ivar is_softmax: True, if output is already softmaxed

Metadata for the SSD object detection head.

@type boxes_outputs: str @ivar boxes_outputs: Output name corresponding to predicted bounding box coordinates. @type scores_outputs: str @ivar scores_outputs: Output name corresponding to predicted bounding box confidence scores.

Metadata for the segmentation head.

@type classes: list @ivar classes: Names of object classes segmented by the model. @type n_classes: int @ivar n_classes: Number of object classes segmented by the model. @type is_softmax: bool @ivar is_softmax: True, if output is already softmaxed

Metadata for the YOLO head.

@type yolo_outputs: list @ivar yolo_outputs: A list of output names for each of the different YOLO grid sizes. @type mask_outputs: list | None @ivar mask_outputs: A list of output names for each mask output. @type protos_outputs: str | None @ivar protos_outputs: Output name for the protos. @type keypoints_outputs: list | None @ivar keypoints_outputs: A list of output names for the keypoints. @type angles_outputs: list | None @ivar angles_outputs: A list of output names for the angles. @type subtype: str @ivar subtype: YOLO family decoding subtype (e.g. yolov5, yolov6, yolov7 etc.) @type n_prototypes: int | None @ivar n_prototypes: Number of prototypes per bbox in YOLO instance segmnetation. @type n_keypoints: int | None @ivar n_keypoints: Number of keypoints per bbox in YOLO keypoint detection. @type is_softmax: bool | None @ivar is_softmax: True, if output is already softmaxed in YOLO instance segmentation

Metadata for the basic head. It allows you to specify additional fields.

@type postprocessor_path: str | None @ivar postprocessor_path: Path to the postprocessor. Metadata of the parser.

Metadata for the object detection head.

@type classes: list @ivar classes: Names of object classes detected by the model. @type n_classes: int @ivar n_classes: Number of object classes detected by the model. @type iou_threshold: float @ivar iou_threshold: Non-max supression threshold limiting boxes intersection. @type conf_threshold: float @ivar conf_threshold: Confidence score threshold above which a detected object is considered valid. @type max_det: int @ivar max_det: Maximum detections per image. @type anchors: list @ivar anchors: Predefined bounding boxes of different sizes and aspect ratios. The innermost lists are length 2 tuples of box sizes. The middle lists are anchors for each output. The outmost lists go from smallest to largest output.

Metadata for the classification head.

@type classes: list @ivar classes: Names of object classes classified by the model. @type n_classes: int @ivar n_classes: Number of object classes classified by the model. @type is_softmax: bool @ivar is_softmax: True, if output is already softmaxed

Metadata for the SSD object detection head.

@type boxes_outputs: str @ivar boxes_outputs: Output name corresponding to predicted bounding box coordinates. @type scores_outputs: str @ivar scores_outputs: Output name corresponding to predicted bounding box confidence scores.

Metadata for the segmentation head.

@type classes: list @ivar classes: Names of object classes segmented by the model. @type n_classes: int @ivar n_classes: Number of object classes segmented by the model. @type is_softmax: bool @ivar is_softmax: True, if output is already softmaxed

Metadata for the YOLO head.

@type yolo_outputs: list @ivar yolo_outputs: A list of output names for each of the different YOLO grid sizes. @type mask_outputs: list | None @ivar mask_outputs: A list of output names for each mask output. @type protos_outputs: str | None @ivar protos_outputs: Output name for the protos. @type keypoints_outputs: list | None @ivar keypoints_outputs: A list of output names for the keypoints. @type angles_outputs: list | None @ivar angles_outputs: A list of output names for the angles. @type subtype: str @ivar subtype: YOLO family decoding subtype (e.g. yolov5, yolov6, yolov7 etc.) @type n_prototypes: int | None @ivar n_prototypes: Number of prototypes per bbox in YOLO instance segmnetation. @type n_keypoints: int | None @ivar n_keypoints: Number of keypoints per bbox in YOLO keypoint detection. @type is_softmax: bool | None @ivar is_softmax: True, if output is already softmaxed in YOLO instance segmentation

Metadata for the basic head. It allows you to specify additional fields.

@type postprocessor_path: str | None @ivar postprocessor_path: Path to the postprocessor.

Public Members

std::optional<std::vector<std::vector<std::vector<double>>>> anchors: Predefined bounding boxes of different sizes and aspect ratios. The innermost lists are length 2 tuples of box sizes. The middle lists are anchors for each output. The outmost lists go from smallest to largest output.

std::optional<std::vector<std::string>> classes: Names of object classes recognized by the model.

std::optional<double> confThreshold: Confidence score threshold above which a detected object is considered valid.

std::optional<double> iouThreshold: Non-max supression threshold limiting boxes intersection.

std::optional<int64_t> maxDet: Maximum detections per image.

std::optional<int64_t> nClasses: Number of object classes recognized by the model.

std::optional<std::string> postprocessorPath: Path to the postprocessor.

std::optional<bool> isSoftmax

True, if output is already softmaxed.

True, if output is already softmaxed in YOLO instance segmentation.

std::optional<std::string> boxesOutputs: Output name corresponding to predicted bounding box coordinates.

std::optional<std::string> scoresOutputs: Output name corresponding to predicted bounding box confidence scores.

std::optional<std::vector<std::string>> anglesOutputs: A list of output names for the angles.

std::optional<std::vector<std::string>> keypointsOutputs: A list of output names for the keypoints.

std::optional<std::vector<std::string>> maskOutputs: A list of output names for each mask output.

std::optional<int64_t> nKeypoints: Number of keypoints per bbox in YOLO keypoint detection.

std::optional<int64_t> nPrototypes: Number of prototypes per bbox in YOLO instance segmnetation.

std::optional<std::string> protosOutputs: Output name for the protos.

std::optional<std::string> subtype: YOLO family decoding subtype (e.g. yolov5, yolov6, yolov7 etc.).

std::optional<std::vector<std::string>> yoloOutputs: A list of output names for each of the different YOLO grid sizes.

nlohmann::json extraParams: Additional parameters