Semantic training spunet

operators.ml3d.semantic_training_spunet(client, data_in_path='/data/files/', out_model_parameters_path='trained_model/model_1', class_names='1,2,3,4,5,6,7,8', feature_names='red,green,blue', point_names='X,Y,Z', label_name='classification', resolution=0.05, max_epochs=500, learning_rate=0.01, batch_size=10, final_div_factor=100, div_factor=10, weight_decay=0.005, channels='32,64,128,256,256,128,96,96', layers='2,3,4,6,2,2,2,2', instance_type='P2')

Spunet Training

semantic_training_spunet( client,

data_in_path=’/data/files/’,

out_model_parameters_path=’trained_model/model_1’,

class_names=’1,2,3,4,5,6,7,8’,

feature_names=’red,green,blue’,

point_names=’X,Y,Z’,

label_name=’classification’,

resolution=0.05,

max_epochs=500,

learning_rate=0.01,

batch_size=10,

final_div_factor=100,

div_factor=10,

weight_decay=0.005,

channels=’32,64,128,256,256,128,96,96’,

layers=’2,3,4,6,2,2,2,2’,

instance_type=’P2’ )

Parameters:

• data_in_path – path to folder that contains the training data

• out_model_parameters_path – path to model

• class_names – comma separated list of class names. Class 0 is always given and is used to denote unlabeled points.

• feature_names – comma separated list of features that are provided

• point_names – comma separated list of point identifiers in (las/laz)

• label_name – label name for (las/laz)

• resolution – resolution of the subsampled point cloud

• max_epochs – maximum number of epochs

• learning_rate – learning rate

• batch_size – batch size

• final_div_factor – final div factor for learning rate

• div_factor – div factor for learning rate

• weight_decay – weight decay

• channels – comma separated list of channels

• layers – comma separated list of layers

• instance_type – type of cloud instance used for processing