Available with Image Analyst license.
Arguments are one of the many ways to control how deep learning models are trained and used. The first table below lists the supported model arguments for training deep learning models. The second table lists the arguments to control how deep learning models are used for inferencing.
Training arguments
The following arguments are available in the Train Deep Learning Model tool for training deep learning models. These arguments vary, depending on the model architecture. You can change the values of these arguments to train a model.
| Model type | Argument | Valid values |
|---|---|---|
Change detector (pixel classification) | attention_type | PAM (Pyramid Attention Module) or BAM (Basic Attention Module). |
chip_size | Integers between 0 and image size. | |
monitor | valid_loss, precision, recall, and f1. | |
ConnectNet (pixel classification) | chip_size | Integers between 0 and image size. |
gaussian_thresh | 0.0 to 1.0. The default is 0.76. | |
monitor | valid_loss, accuracy, miou, and dice. | |
mtl_model | linknet or hourglass. | |
orient_bin_size | A positive number. The default is 20. | |
orient_theta | A positive number. The default is 8. | |
DeepLabv3 (pixel classification) | chip_size | Integers between 0 and image size. |
class_balancing | true or false. | |
focal_loss | true or false. | |
ignore_classes | Valid class values. | |
monitor | valid_loss and accuracy. | |
mixup | true or false. | |
Image captioner (image translation) | chip_size | Integers between 0 and image size. |
The decode_params argument is composed of the following six parameters:
| The default value is {'embed_size':100, 'hidden_size':100, 'attention_size':100, 'teacher_forcing':1, 'dropout':0.1, 'pretrained_emb':False}. | |
monitor | valid_loss, accuracy, corpus_bleu, and multi_label_fbeta. | |
MMDetection (object detection) | chip_size | Integers between 0 and image size. |
model | atss, carafe, cascade_rcnn, cascade_rpn, dcn, detectors, double_heads, dynamic_rcnn, empirical_attention, fcos, foveabox, fsaf, ghm, hrnet, libra_rcnn, nas_fcos, pafpn, pisa, regnet, reppoints, res2net, sabl, and vfnet. | |
model_weight | true or false. | |
MMSegmentation (pixel classification) | chip_size | Integers between 0 and image size. |
model | ann, apcnet, ccnet, cgnet, danet, deeplabv3, deeplabv3plus, dmnet , dnlnet, emanet, encnet, fastscnn, fcn, gcnet, hrnet, mobilenet_v2, mobilenet_v3, nonlocal_net, ocrnet, ocrnet_base, pointrend, psanet, pspnet, resnest, sem_fpn, unet, and upernet. | |
model_weight | true or false. | |
Multi Task Road Extractor (pixel classification) | chip_size | Integers between 0 and image size. |
gaussian_thresh | 0.0 to 1.0. The default is 0.76. | |
monitor | valid_loss, accuracy, miou, and dice. | |
mtl_model | linknet or hourglass. | |
orient_bin_size | A positive number. The default is 20. | |
orient_theta | A positive number. The default is 8. | |
Pyramid Scene Parsing Network (pixel classification) | chip_size | Integers between 0 and image size. |
class_balancing | true or false. | |
focal_loss | true or false. | |
ignore_classes | Valid class values. | |
monitor | valid_loss or accuracy. | |
mixup | true or false. | |
pyramid_sizes | [convolution layer 1, convolution layer 2, ... , convolution layer n] | |
use_net | true or false. | |
RetinaNet (object detection) | chip_size | Integers between 0 and image size. |
monitor | valid_loss or average_precision. | |
ratios | Ratio value 1, ratio vale 2, ratio value 3. The default is 0.5,1,2. | |
scales | [scale value 1, scale value 2, scale value 3] The default is [1, 0.8, 0.63]. | |
Single Shot Detector (object detection) | chip_size | Integers between 0 and image size. |
grids | Integer values greater than 0. | |
monitor | valid_lossor average_precision. | |
ratios | [horizontal value, vertical value] | |
zooms | The zoom value in which 1.0 is normal zoom. | |
U-Net (pixel classification) | chip_size | Integers between 0 and image size. |
class_balancing | true or false. | |
focal_loss | true or false. | |
ignore_classes | Valid class values. | |
monitor | valid_loss or accuracy. | |
mixup | true or false. |
Inferencing arguments
The following arguments are available to control how deep learning models are trained for inferencing. The information from the Model Definition parameter will be used to populate Arguments in the inferencing tools. These arguments vary, depending on the model architecture. ArcGIS pretrained models and custom deep learning models may have additional arguments that the tool supports.
| Argument | Inference type | Valid values |
|---|---|---|
| batch_size | Classify Objects Classify Pixels Detect Change Detect Objects | Integer values greater than 0; it's usually an integer that is a power of 2n. |
| direction | Classify Pixels | Available options are AtoB and BtoA. The argument is only available for the CycleGAN architecture. |
| exclude_pad_detections | Detect Objects | true or false. The argument is available for SSD, RetinaNet, YOLOv3, DETReg, MMDetection, and Faster RCNN only. |
| merge_policy | Classify Pixels Detect Objects | Available options are mean, max, and min. For the Classify Pixels Using Deep Learning tool, the argument is available for the MultiTaskRoadExtractor and ConnectNet architectures. If IsEdgeDetection is present in the model's .emd file, BDCNEdgeDetector, HEDEdgeDetector, and MMSegmentation are also available architectures. For the Detect Objects Using Deep Learning tool, the argument is only available for MaskRCNN. |
| nms_overlap | Detect Objects | A floating point value of 0.0 to 1.0. The default is 0.1. |
output_classified_raster | Detect Objects | The file path and name for the output classified raster. The argument is only available for MaXDeepLab. |
padding | Classify Pixels Detect Change Detect Objects | Integer values greater than 0 and less than half the tile size value. |
| predict_background | Classify Pixels | true or false. The argument is available for UNET, PSPNET, DeepLab, and MMSegmentation. |
return_probability_raster | Classify Pixels | true or false. If ArcGISLearnVersion is 1.8.4 or later in the model's .emd file, the MultiTaskRoadExtractor and ConnectNet architectures are available. If ArcGISLearnVersion is 1.8.4 or later and IsEdgeDetection is present in the model's .emd file, the BDCNEdgeDetector, HEDEdgeDetector, and MMSegmentation architectures are also available. |
| score_threshold | Classify Objects | 0 to 1.0. |
| test_time_augmentation | Classify Objects Classify Pixels | true or false. |
| threshold | Classify Pixels Detect Objects | 0 to 1.0. For the Classify Pixels Using Deep Learning tool, if ArcGISLearnVersion is 1.8.4 or later in the model's .emd file, the MultiTaskRoadExtractor and ConnectNet architectures are available. If ArcGISLearnVersion is 1.8.4 or later and IsEdgeDetection is present in the model's .emd file, the BDCNEdgeDetector, HEDEdgeDetector, and MMSegmentation architectures are also available. For the Detect Objects Using Deep Learning tool, the argument is available for all model architectures. |
| thinning | Classify Pixels | true or false. If IsEdgeDetection is present in the model's .emd file, BDCNEdgeDetector, HEDEdgeDetector, and MMSegmentation are available architectures. |
| tile_size | Classify Pixels Detect Objects | Integer values greater than 0 and less than the size of the image. For the Classify Pixels Using Deep Learning tool, the argument is only available for the CycleGAN architecture. For the Detect Objects Using Deep Learning tool, the argument is only available for MaskRCNN. |