dlpy.layers.FastRCNN

class dlpy.layers.FastRCNN(name=None, act='AUTO', class_number=20, detection_threshold=0.5, max_label_per_image=200, max_object_num=50, nms_iou_threshold=0.3, src_layers=None, **kwargs)

FastRCNN layer

Parameters

namestring, optional

Specifies the name of the layer.

actstring, optional

Specifies the activation function. Valid Values: AUTO, IDENTITY, Default: AUTO

class_numberint, optional

Specifies the number of categories for the objects in the layer Default: 20

detection_thresholdfloat, optional

Specifies the threshold for object detection. Default: 0.5

max_label_per_imageint, optional

Specifies the maximum number of labels per training image. Default: 200

max_object_numint, optional

Specifies the maximum number of object to detect Default: 50

nms_iou_thresholdfloat, optional

Specifies the IOU threshold of maximum suppression in object detection Default: 0.3

src_layersiter-of-Layers, optional

Specifies the layers directed to this layer.

Returns

FastRCNN

__init__(name=None, act='AUTO', class_number=20, detection_threshold=0.5, max_label_per_image=200, max_object_num=50, nms_iou_threshold=0.3, src_layers=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([name, act, class_number, …])	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

Attributes

can_be_last_layer
kernel_size
layer_id
num_bias
num_weights
number_of_instances
output_size
rnn_summary	Return a DataFrame containing the layer information for rnn models
summary	Return a DataFrame containing the layer information
type
type_desc
type_label