Train Deep Learning Model (Image Analyst)

Disponible con licencia de Image Analyst.

Resumen

Entrena un modelo de aprendizaje profundo utilizando la salida de la herramienta Exportar datos de entrenamiento para aprendizaje profundo.

Uso

• Para configurar su equipo para usar marcos de aprendizaje profundo en ArcGIS Pro, consulte Instalar marcos de aprendizaje profundo para ArcGIS.

• This tool trains using the PyTorch deep learning framework. PyTorch must be installed on your machine to successfully run this tool.

• The input training data for this tool must include the images and labels folders that are generated from the Export Training Data For Deep Learning tool.

• Para obtener información sobre los requisitos para ejecutar esta herramienta y problemas que puede encontrarse, consulte Preguntas más frecuentes sobre aprendizaje profundo.

• Para obtener más información sobre el aprendizaje profundo, consulte Aprendizaje profundo en ArcGIS Pro.

Sintaxis

TrainDeepLearningModel(in_folder, out_folder, {max_epochs}, {model_type}, {batch_size}, {arguments}, {learning_rate}, {backbone_model}, {pretrained_model}, {validation_percentage}, {stop_training}, {freeze})

Parámetro	Explicación	Tipo de datos
in_folder	The folder containing the image chips, labels, and statistics required to train the model. This is the output from the Export Training Data For Deep Learning tool. To train a model, the input images must be 8-bit rasters with three bands.	Folder
out_folder	The output folder location that will store the trained model.	Folder
max_epochs (Opcional)	The maximum number of epochs for which the model will be trained. A maximum epoch of one means the dataset will be passed forward and backward through the neural network one time. The default value is 20.	Long
model_type (Opcional)	Specifies the model type to use to train the deep learning model. SSD —The Single Shot Detector (SSD) approach will be used to train the model. SSD is used for object detection. The input training data for this model type uses the Pascal Visual Object Classes metadata format. UNET —The U-Net approach will be used to train the model. U-Net is used for pixel classification. FEATURE_CLASSIFIER — The Feature Classifier approach will be used to train the model. This is used for object or image classification. PSPNET —The Pyramid Scene Parsing Network (PSPNET) approach will be used to train the model. PSPNET is used for pixel classification. RETINANET —The RetinaNet approach will be used to train the model. RetinaNet is used for object detection. The input training data for this model type uses the Pascal Visual Object Classes metadata format. MASKRCNN —The MaskRCNN approach will be used to train the model. MaskRCNN is used for object detection. It is used for instance segmentation, which is the automatic delineation of objects in an image, such as detecting building footprints or swimming pools. It uses the MaskRCNN metadata format for training data as input. Class values for input training data must start at 1. This model type can only be trained using a CUDA-enabled GPU.	String
batch_size (Opcional)	The number of training samples to be processed for training at one time. The default value is 2. If you have a powerful GPU, this number can be increased to 8, 16, 32, or 64.	Long
arguments [arguments,...] (Opcional)	Los argumentos de función se definen en la clase de función ráster de Python. Aquí es donde enumera los argumentos y parámetros de aprendizaje profundo adicionales para experimentos y refinamiento, como un umbral de confianza para ajustar la sensibilidad. Los nombres de los argumentos se rellenan a partir de la lectura del módulo de Python. When you choose SSD as the model_type parameter value, the arguments parameter will be populated with the following arguments: grids—The number of grids the image will be divided into for processing. Setting this argument to [4] means the image will be divided into 4 x 4 or 16 grid cells. The default is [4, 2, 1], meaning there will be 21 grid cells ([4 x 4] + [2 x 2] + [1 x 1] = 21). zooms—The number of zoom levels each grid cell will be scaled up or down. Setting this argument to [1] means all the grid cells will remain at the same size or zoom level. A zoom level of [2] means all the grid cells will become twice as large (zoomed in 100 percent). Providing a list of zoom levels means all the grid cells will be scaled using all the numbers in the list. The default is [0.7, 1.0, 1.3]. ratios—The list of aspect ratios to use for the anchor boxes. In object detection, an anchor box represents the ideal location, shape, and size of the object being predicted. Setting this argument to [[1.0,1.0], [1.0, 0.5]] means the anchor box is a square (1:1) or a rectangle in which the horizontal side is half the size of the vertical side (1:0.5). The default is [[1, 1], [1, 0.5], [0.5, 1]]. When you choose PSPNET as the model_type parameter value, the arguments parameter will be populated with the following arguments: USE_UNET_DECODER—The U-Net decoder will be used to recover data once the pyramid pooling is complete. The default is True. PYRAMID_SIZE—The number and size of convolution layers to be applied to the different subregions. The default is [1,2,3,6]. When you choose RETINANET as the model_type parameter value, the arguments parameter will be populated with the following arguments: SCALES—The number of scale levels each cell will be scaled up or down. The default is [1, 0.8, 0.63]. RATIOS—The aspect ratio of the anchor box. The default is [0.5,1,2].	Value Table
learning_rate (Opcional)	The rate at which existing information will be overwritten with newly acquired information throughout the training process. If no value is specified, the optimal learning rate will be extracted from the learning curve during the training process.	Double
backbone_model (Opcional)	Specifies the preconfigured neural network to be used as an architecture for training the new model. This method is known as Transfer Learning. DENSENET121 —The preconfigured model will be a dense network trained on the ImageNET Dataset that contains more than a million images and is 121 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation. DENSENET161 —The preconfigured model will be a dense network trained on the ImageNET Dataset that contains more than a million images and is 161 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation. DENSENET169 —The preconfigured model will be a dense network trained on the ImageNET Dataset that contains more than a million images and is 169 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation. DENSENET201 —The preconfigured model will be a dense network trained on the ImageNET Dataset that contains more than a million images and is 201 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation. MOBILENET_V2 —This preconfigured model is trained on the ImageNet Database and is 54 layers deep geared toward Edge device computing, since it uses less memory. MASKRCNN50_FPN —This preconfigured model was trained for object detection with backbone RESNET50 with Feature Pyramid Networks to detect objects at different scales. RESNET18 —The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than a million images and is 18 layers deep. RESNET34 —The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than a million images and is 34 layers deep. This is the default. RESNET50 —The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than a million images and is 50 layers deep. RESNET101 —The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than a million images and is 101 layers deep. RESNET152 —The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than a million images and is 152 layers deep. VGG11 —The preconfigured model will be a convolution neural network trained on the ImageNET Dataset that contains more than a million images to classify images into 1000 object categories and is 11 layers deep. VGG11_BN —This preconfigured model is based on the VGG network but with batch normalization, which means each layer in the network is normalized. It trained on the ImageNet dataset and has 11 layers. VGG13 —The preconfigured model will be a convolution neural network trained on the ImageNET Dataset that contains more than a million images to classify images into 1000 object categories and is 13 layers deep. VGG13_BN —This preconfigured model is based on the VGG network but with batch normalization, which means each layer in the network is normalized. It trained on the ImageNet dataset and has 13 layers. VGG16 —The preconfigured model will be a convolution neural network trained on the ImageNET Dataset that contains more than a million images to classify images into 1000 object categories and is 16 layers deep. VGG16_BN —This preconfigured model is based on the VGG network but with batch normalization, which means each layer in the network is normalized. It trained on the ImageNet dataset and has 16 layers. VGG19 —The preconfigured model will be a convolution neural network trained on the ImageNET Dataset that contains more than a million images to classify images into 1000 object categories and is 19 layers deep. VGG19_BN —This preconfigured model is based on the VGG network but with batch normalization, which means each layer in the network is normalized. It trained on the ImageNet dataset and has 19 layers.	String
pretrained_model (Opcional)	The pretrained model to be used for fine tune training the new model. The input is an Esri Model Definition file (.emd). A pretrained model with similar classes can be fine-tuned to fit the new model. For example, an existing model that has been trained for cars can be fine-tuned to train a model that identifies trucks. The pretrained model must have been trained with the same model type and backbone model that will be used to train the new model.	File
validation_percentage (Opcional)	The percentage of training samples that will be used for validating the model. The default value is 10.	Double
stop_training (Opcional)	Specifies whether early stopping will be implemented. STOP_TRAINING —The model training will stop when the model is no longer improving, regardless of the max_epochs parameter value specified. This is the default. CONTINUE_TRAINING —The model training will continue until the max_epochs parameter value is reached.	Boolean
freeze (Opcional)	Specifies whether to freeze the backbone layers in the pretrained model, so that the weights and biases remain as originally designed. FREEZE_MODEL —The predefined weights and biases will not be altered in the backbone_model. This is the default. UNFREEZE_MODEL —The weights and biases of the backbone_model may be altered, to better fit your training samples. This takes more time to process but usually gets better results.	Boolean

Salida derivada

Nombre	Explicación	Tipo de datos
out_model_file	The output trained model file.	File

Muestra de código

TrainDeepLearningModel example 1 (Python window)

This example trains a tree classification model using the U-Net approach.

# Import system modules  
import arcpy  
from arcpy.ia import *  
 
# Check out the ArcGIS Image Analyst extension license 
arcpy.CheckOutExtension("ImageAnalyst") 
 
# Execute 
TrainDeepLearningModel(r"C:\DeepLearning\TrainingData\Roads_FC", 
     r"C:\DeepLearning\Models\Fire", 40, "UNET", 16, "# #", None, 
     "RESNET34", None, 10, "STOP_TRAINING", "FREEZE_MODEL")

TrainDeepLearningModel example 2 (stand-alone script)

This example trains an object detection model using the SSD approach.

# Import system modules  
import arcpy  
from arcpy.ia import *  
 
# Check out the ArcGIS Image Analyst extension license 
arcpy.CheckOutExtension("ImageAnalyst") 
 
#Define input parameters
in_folder = "C:\\DeepLearning\\TrainingData\\Cars" 
out_folder = "C:\\Models\\Cars"
max_epochs = 100
model_type = "SSD"
batch_size = 2
arg = "grids '[4, 2, 1]';zooms '[0.7, 1.0, 1.3]';ratios '[[1, 1], [1, 0.5], [0.5, 1]]'"
learning_rate = 0.003
backbone_model = "RESNET34" 
pretrained_model = "C:\\Models\\Pretrained\\vehicles.emd"
validation_percent = 10
stop_training = "STOP_TRAINING"
freeze = "FREEZE_MODEL"


# Execute
TrainDeepLearningModel(in_folder, out_folder, max_epochs, model_type, 
     batch_size, arg, learning_rate, backbone_model, pretrained_model, 
     validation_percent, stop_training, freeze)

Entornos

Espacio de trabajo actual, Extensión, Tipo de procesador, Id. de GPU, Factor de procesamiento en paralelo, Espacio de trabajo temporal

Información de licenciamiento

• Basic: Requiere Image Analyst
• Standard: Requiere Image Analyst
• Advanced: Requiere Image Analyst