Available with Image Analyst license.
Summary
Trains a deep learning model using the output from the Export Training Data For Deep Learning tool.
Usage
This tool trains a deep learning model using deep learning frameworks.
To set up your machine to use deep learning frameworks in ArcGIS Pro, see Install deep learning frameworks for ArcGIS.
This tool can also be used to fine-tune an existing trained model. For example, an existing model that has been trained for cars can be fine-tuned to train a model that identifies trucks.
To run this tool using GPU, set the Processor Type environment to GPU. If you have more than one GPU, specify the GPU ID environment instead.
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.
For information about requirements for running this tool and issues you may encounter, see Deep Learning Frequently Asked Questions.
For more information about deep learning, see Deep learning in ArcGIS Pro.
Syntax
TrainDeepLearningModel(in_folder, out_folder, {max_epochs}, {model_type}, {batch_size}, {arguments}, {learning_rate}, {backbone_model}, {pretrained_model}, {validation_percentage}, {stop_training}, {freeze})
Parameter | Explanation | Data Type |
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 (Optional) | 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 (Optional) | Specifies the model type to use to train the deep learning model.
| String |
batch_size (Optional) | 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,...] (Optional) | The function arguments are defined in the Python raster function class. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for adjusting sensitivity. The names of the arguments are populated from reading the Python module. When you choose SSD as the model_type parameter value, the arguments parameter will be populated with the following arguments:
When you choose any of the pixel classification models such as PSPNET, UNET, or DEEPLAB as the model_type parameter value, the arguments parameter will be populated with the following arguments:
When you choose RETINANET as the model_type parameter value, the arguments parameter will be populated with the following arguments:
All model types support the chip_size argument, which is the chip size of the tiles in the training samples. The image chip size is extracted from the .emd file from the folder specified in the in_folder parameter. | Value Table |
learning_rate (Optional) | 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 (Optional) | Specifies the preconfigured neural network to be used as an architecture for training the new model. This method is known as Transfer Learning.
| String |
pretrained_model (Optional) | The pretrained model to be used for fine-tune training the new model. The input is an Esri Model Definition file (.emd) or a deep learning package file (.dlpk). A pretrained model with similar classes can be fine-tuned to fit the new model. 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 (Optional) | The percentage of training samples that will be used for validating the model. The default value is 10. | Double |
stop_training (Optional) | Specifies whether early stopping will be implemented.
| Boolean |
freeze (Optional) | Specifies whether the backbone layers in the pretrained model will be frozen, so that the weights and biases remain as originally designed.
| Boolean |
Derived Output
Name | Explanation | Data Type |
out_model_file | The output trained model file. | File |
Code sample
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")
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)
Environments
Licensing information
- Basic: Requires Image Analyst
- Standard: Requires Image Analyst
- Advanced: Requires Image Analyst