dlpy.layers.Detection¶

class dlpy.layers.Detection(name=None, act='AUTO', detection_model_type=None, anchors=None, softmax_for_class_prob=None, coord_type=None, class_number=None, grid_number=None, predictions_per_grid=None, do_sqrt=None, coord_scale=None, object_scale=None, prediction_not_a_object_scale=None, class_scale=None, detection_threshold=None, iou_threshold=None, random_boxes=None, src_layers=None, max_boxes=None, max_label_per_image=None, match_anchor_size=None, num_to_force_coord=None, force_coord_scale=None, **kwargs)¶

Bases: dlpy.layers.Layer

Detection layer

Parameters:

name : string, optional: Specifies the name of the layer.
act : string, optional: Specifies the activation function.
Valid Values: AUTO, IDENTITY, LOGISTIC, SIGMOID, TANH, RECTIFIER, RELU, SOFPLUS, ELU, LEAKY, FCMP
Default: AUTO
detection_model_type : int, optional: Specifies the type of the object detection model.
Valid Values: YOLOV1, YOLOV2
Default: YOLOV2
anchors : iter-of-floats, optional: Specifies the anchor box values. Anchor box values are a list of scalar value pairs that represent the normalized box sizes in X and Y direction for objects to be detected. The normalized box sizes are calculated by dividing the box size in pixels by the grid size.
softmax_for_class_prob : bool, optional: Specifies whether to perform Softmax on class probability per predicted object.
Default: True
coord_type : int, optional: Specifies the coordinates format type in the input label and detection result.
Valid Values: RECT, COCO, YOLO
Default: RECT
class_number : int: Specifies the number of classes to detection in the detection layer.
grid_number : int: Specifies the number of grids per side in the detection layer.
predictions_per_grid : int: Specifies the number of predictions to generate in the detection layer.
do_sqrt : bool, optional: Specifies whether to apply the SQRT function to width and height of the object for the cost function.
Default: True
coord_scale : float, optional: Specifies the weight for the cost function in the detection layer, when objects exist in the grid.
object_scale : float, optional: Specifies the weight for object detected for the cost function in the detection layer.
prediction_not_a_object_scale : float, optional: Specifies the weight for the cost function in the detection layer, when objects do not exist in the grid.
class_scale : float, optional: Specifies the weight for the class of object detected for the cost function in the detection layer.
detection_threshold : float, optional: Specifies the threshold for object detection.
iou_threshold : float, optional: Specifies the IOU Threshold of maximum suppression in object detection.
random_boxes : bool, optional: Randomizing boxes when loading the bounding box information.
Default: False
src_layers : iter-of-Layers, optional: Specifies the layers directed to this layer.
max_boxes : int, optional: Specifies the maximum number of overall predictions allowed in the detection layer.
max_label_per_image : int, optional: The maximum number of labels per image
match_anchor_size : bool, optional: Whether to force the predicted box match the anchor boxes in sizes for all predictions
num_to_force_coord : int, optional: The number of leading chunk of images in training when the algorithm forces predicted objects in each grid to be equal to the anchor box sizes, and located at the grid center
force_coord_scale : float, optional: The scale for location error during the training period while forcing the predicted boxes to have default sizes/locations

Returns:

Detection

__init__(name=None, act='AUTO', detection_model_type=None, anchors=None, softmax_for_class_prob=None, coord_type=None, class_number=None, grid_number=None, predictions_per_grid=None, do_sqrt=None, coord_scale=None, object_scale=None, prediction_not_a_object_scale=None, class_scale=None, detection_threshold=None, iou_threshold=None, random_boxes=None, src_layers=None, max_boxes=None, max_label_per_image=None, match_anchor_size=None, num_to_force_coord=None, force_coord_scale=None, **kwargs)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([name, act, detection_model_type, …])	Initialize self.
count_instances()
format_name([block_num, local_count])	Format the name of the layer
get_number_of_instances()
to_model_params()	Convert the model configuration to CAS action parameters