Semantic training pt v3m1 folder
- operators.ml3d.semantic_training_pt_v3m1_folder(client, data_in_folder='/data_in_folder', out_model_parameters_path='trained_model/model_ptv2m2', 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, worker_instance_type='P2', manager_instance_type='small', extension_data_in_path='.laz', skip_existing_files=False)
- Pt v3m1 Training
- semantic_training_pt_v3m1_folder(client,data_in_folder=’/data_in_folder’,out_model_parameters_path=’trained_model/model_ptv2m2’,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,worker_instance_type=’P2’,manager_instance_type=”small”,extension_data_in_folder=”./data/files/”,skip_existing_files = False )
- Parameters:
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
data_in_folder – folder to folder that contains the training data
worker_instance_type – cloud instance type of worker nodes
manager_instance_type – cloud instance type of manager node
extension_data_in_folder – File extension of files in folder for data_in_folder
skip_existing_files – skip files that already exist in the output folder