(Required)

This is the input location for the training sample data. It can be the path of the output location in the file share raster data store or a shared file system path. The training sample data folder must be the output from the ExportTrainingDataforDeepLearning operation, containing image and label folders, as well as the JSON model definition file written by the tool.

The following are file share raster store path examples:

//File share raster store path examples
in_folder=/rasterStores/yourRasterStoreFolderName/trainingSampleData
in_folder=/fileShares/yourFileShareFolderName/trainingSampleData

//File share path example
in_folder=\\serverName\deepLearning\trainingSampleData

(Required)

This is the output location for the trained deep learning model package (.dlpk). It can be a JSON object representing the output .dlpk name that will be added as a portal item or a string of the folder path in the file share data store. The file share data store must be registered on the server.

//Output dlpk name
output_name={"name": "trainedModel"}
output_name={"name": "trainedModel","folderId":"dfwerfbd3ec25584d0d8f4"}

//File share data store path
output_name=“/fileShares/filesharename/folder”

//File share data store path:
output_name={"uri":"/fileShares/yourFileShareFolderName/trainedModel"}

(Required)

The model type to use for training the deep learning model. This parameter supports model types for image translation, object classification, object detection, object tracker, and pixel classification. The model types that are supported for each type of processing and the supported values for this parameter are listed below.

Image translation values: PIX2PIX | CYCLEGAN | SUPERRESOLUTION | PIX2PIXHD

Object classification values: FEATURE_CLASSIFIER | IMAGECAPTIONER

Object detection values: SSD | RETINANET | MASKRCNN | YOLOV3 | FASTERRCNN | MMDETECTION | DETREG

Object tracker values: SIAMMASK | DEEPSORT

Panoptic segmentation values: MAXDEEPLAB

Pixel classification values: UNET | PSPNET | DEEPLAB | BDCN_EDGEDETECTOR | HED_EDGEDETECTOR | MULTITASK_ROADEXTRACTOR | CONNECTNET | CHANGEDETECTOR | MMSEGMENTATION

(Optional)

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 set model_type to SSD, the following arguments will be used:

• 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. If no value is specified, the optimal grid value will be calculated based on the input imagery.
• 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 1.0.
• 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 [1.0, 1.0].

When you set model_type to any of the pixel classification models (PSPNET, UNET, or DEEPLAB), the following arguments will be used:

• USE_UNET—The U-Net decoder will be used to recover data once the pyramid pooling is complete. The default is True. This argument is specific to the PSPNET model.
• PYRAMID_SIZES—The number and size of convolution layers to be applied to the different subregions. The default is [1,2,3,6]. This argument is specific to the PSPNET model.
• MIXUP—Specifies whether mixup augmentation and mixup loss will be used. The default is False.
• CLASS_BALANCING—Specifies whether the cross-entropy loss inverse will be balanced to the frequency of pixels per class. The default is False.
• FOCAL_LOSS—Specifies whether focal loss will be used. The default is False.
• IGNORE_CLASSES—The list of class values on which the model will incur loss.

When you set model_type to RETINANET, the following arguments will be used:

• 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].
• MONITOR—Specifies the metric to monitor when checkpointing and early stopping of training. Available metrics are valid_loss, accuracy, miou, and dice. The default is valid_loss.

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 in the input folder.

Syntax: The value pairs of arguments and their values.

arguments={"name1": "value1", "name2": "value2"}

(Optional)

The number of training samples to be processed for training at one time. If the server has a powerful GPU, this number can be increased to 16, 36, 64, and so on.

batch_size=4

(Optional)

The maximum number of epochs for training the model. One epoch means the whole training dataset will be passed forward and backward through the deep neural network once.

max_epochs=20

(Optional)

The rate at which the weights are updated during the training. It is a small positive value in the range between 0.0 and 1.0. If the learning rate is set to 0, it will extract the optimal learning rate from the learning curve during the training process.

learning_rate=0

(Optional)

Specifies the preconfigured neural network to be used as an architecture for training the new model. See the Backbone model values section below for more information.

Values: DARKNET53 | DENSENET121 | DENSENET161 | DENSENET169 | DENSENET201 | MOBILENET_V2| REID_V1 | REID_V2 | RESNET18 | RESNET34 | RESNET50 | RESNET101 | RESNET152 | VGG11 | VGG11_BN | VGG13 | VGG13_BN | VGG16 | VGG16_BN | VGG19 | VGG19_BN

backbone_model=RESNET34

(Optional)

The percentage of training sample data that will be used for validating the model.

validation_percent=10

(Optional)

The pretrained model to be used for fine-tuning the new model. It is a .dlpk portal item.

pretrained_model={"itemId": "8cfbd3ec25584d0d8fed23b8ff7c43b"}

(Optional)

Specifies whether early stopping will be implemented. If true, the model training will stop when the model is no longer improving, regardless of the maximum epochs specified. This is the default. If false, the model training will continue until the maximum epochs is reached.

Values: true | false

(Optional)

Overwrites an existing deep learning model package (.dlpk) portal item with the same name.

If the output_name parameter uses the file share data store path, the overwriteModel parameter is not applied.

• True—The portal .dlpk item will be overwritten.
• False—The portal .dlpk item will not be overwritten. This is the default.

(Optional)

Environment settings that affect task operation. This parameter has the following settings:

• extent—A bounding box that defines the analysis area.
• cellSize—The output raster will have the resolution specified by cell size.
• processorType—The specified processor (CPU or GPU) will be used for the analysis.
• parallelProcessingFactor—The number of logical processes across which a tool will operate.

context={"cellSize": "20","processorType": "GPU"}

Specifies whether the backbone layers in the pretrained model will be frozen so that the weights and biases in the backbone layers remain unaltered. If true, the predefined weights and biases will not be altered in the backboneModel value. This is the default. If false, the weights and biases of the backboneModel value may be altered to better fit the training samples. This may take more time to process but typically produces better results.

Values: true | false

The response format. The default response format is html.

Values: html | json | pjson

The preconfigured model will be a convolutional neural network trained on the ImageNet dataset that contains more than 1 million images and is 53 layers deep.

The preconfigured model will be a dense network trained on the ImageNet dataset that contains more than 1 million images and is 121 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation.

The preconfigured model will be a dense network trained on the ImageNet dataset that contains more than 1 million images and is 161 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation.

The preconfigured model will be a dense network trained on the ImageNet dataset that contains more than 1 million images and is 169 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation.

The preconfigured model will be a dense network trained on the ImageNet dataset that contains more than 1 million images and is 201 layers deep. Unlike RESNET, which combines the layer using summation, DenseNet combines the layers using concatenation.

The preconfigured model trained on the ImageNet database and is 54 layers deep geared toward Edge device computing, since it uses less memory.

The preconfigured model will be a residual network trained on the ImageNet dataset that contains more than 1 million images and is 18 layers deep.

The preconfigured model will be a residual network trained on the ImageNet dataset that contains more than 1 million images and is 34 layers deep. This is the default.

The preconfigured model will be a residual network trained on the ImageNet dataset that contains more than 1 million images and is 50 layers deep.

The preconfigured model will be a residual network trained on the ImageNet dataset that contains more than 1 million images and is 101 layers deep.

The preconfigured model will be a residual network trained on the ImageNet dataset that contains more than 1 million images and is 152 layers deep.

The preconfigured model will be a convolution neural network trained on the ImageNet dataset that contains more than 1 million images to classify images into 1,000 object categories and is 11 layers deep.

The preconfigured model is based on the VGG network but with batch normalization, which normalizes each layer in the network. It trained on the ImageNet dataset and has 11 layers.

The preconfigured model will be a convolution neural network trained on the ImageNet dataset that contains more than 1 million images to classify images into 1,000 object categories and is 13 layers deep.

The preconfigured model is based on the VGG network but with batch normalization, which normalizes each layer in the network. It trained on the ImageNet dataset and has 13 layers.

The preconfigured model will be a convolution neural network trained on the ImageNet dataset that contains more than 1 million images to classify images into 1,000 object categories and is 16 layers deep.

The preconfigured model is based on the VGG network but with batch normalization, which normalizes each layer in the network. It trained on the ImageNet dataset and has 16 layers.

The preconfigured model will be a convolution neural network trained on the ImageNet dataset that contains more than 1 million images to classify images into 1,000 object categories and is 19 layers deep.

The preconfigured model is based on the VGG network but with batch normalization, which normalizes each layer in the network. It trained on the ImageNet dataset and has 19 layers.