Train Deep Learning Model (Image Analyst)

Доступно с лицензией Image Analyst.

Краткая информация

Тренирует модель глубокого обучения с использованием результатов работы инструмента Экспорт обучающих данных для глубокого обучения.

Использование

  • This tool trains a deep learning model using deep learning frameworks.

  • Чтобы настроить компьютер на работу в среде глубокого обучения в ArcGIS Pro, см. раздел Установка сред глубокого обучения для ArcGIS.

  • If you will be training models in a disconnected environment, see Installation for Disconnected Environment for additional information.

  • 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.

  • Информацию о требованиях к запуску этого инструмента и проблемах, с которыми вы можете столкнуться, см. в разделе Часто задаваемые вопросы по глубокому обучению.

  • Дополнительную информацию о глубоком обучении см. в разделе Глубокое обучение в ArcGIS Pro.

Параметры

ПодписьОписаниеТип данных
Input Training Data

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
Output Model

The output folder location that will store the trained model.

Folder
Max Epochs
(Дополнительный)

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
(Дополнительный)

Specifies the model type that will be used to train the deep learning model.

  • Single Shot Detector (Object detection) —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.
  • U-Net (Pixel classification) —The U-Net approach will be used to train the model. U-Net is used for pixel classification.
  • Feature classifier (Object classification) —The Feature Classifier approach will be used to train the model. This is used for object or image classification.
  • Pyramid Scene Parsing Network (Pixel classification) —The Pyramid Scene Parsing Network (PSPNET) approach will be used to train the model. PSPNET is used for pixel classification.
  • RetinaNet (Object detection) —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 (Object detection) —The MaskRCNN approach will be used to train the model. MaskRCNN is used for object detection. This approach is used for instance segmentation, which is precise delineation of objects in an image. This model type can be used to detect building footprints. 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.
  • YOLOv3 (Object detection) —The YOLOv3 approach will be used to train the model. YOLOv3 is used for object detection.
  • DeepLabV3 (Pixel classification) —The DeepLabV3 approach will be used to train the model. DeepLab is used for pixel classification.
  • FasterRCNN (Object detection) —The FasterRCNN approach will be used to train the model. FasterRCNN is used for object detection.
  • BDCN Edge Detector (Pixel classification) — The Bi-Directional Cascade Network (BDCN) architecture will be used to train the model. The BDCN Edge Detector is used for pixel classification. This approach is useful to improve edge detection for objects at different scales.
  • HED Edge Detector (Pixel classification) — The Holistically-Nested Edge Detection (HED) architecture will be used to train the model. The HED Edge Detector is used for pixel classification. This approach is useful to in edge and object boundary detection.
  • Multi Task Road Extractor (Pixel classification) — The Multi Task Road Extractor architecture will be used to train the model. The Multi Task Road Extractor is used for pixel classification. This approach is useful for road network extraction from satellite imagery.
  • ConnectNet (Pixel classification) —The ConnectNet architecture will be used to train the model. ConnectNet is used for pixel classification. This approach is useful for road network extraction from satellite imagery.
  • Pix2Pix (Image translation) —The Pix2Pix approach will be used to train the model. Pix2Pix is used for image to image translation. This approach creates a model object that generates images of one type to another. The input training data for this model type uses the Export Tiles metadata format.
  • CycleGAN (Image translation) —The CycleGAN approach will be used to train the model. CycleGAN is used for image-to-image translation. This approach creates a model object that generates images of one type to another. This approach is unique in that the images to be trained do not need to overlap. The input training data for this model type uses the CycleGAN metadata format.
  • Super-resolution (Image translation) —The super-resolution approach will be used to train the model. Super-resolution is used for image-to-image translation. This approach creates a model object that increases the resolution and improves the quality of images. The input training data for this model type uses the Export Tiles metadata format.
  • Change detector (Pixel Classification) —The Change Detector approach will be used to train the model. Change Detector is used for pixel classification. This approach creates a model object that uses two spatial-temporal images to create a classified raster of the change. The input training data for this model type uses the Classified Tiles metadata format.
  • Image captioner (Image Translation) —The Image Captioner approach will be used to train the model. Image Captioner is used for image-to-text translation. This approach creates a model that generates text captions for an image.
String
Batch Size
(Дополнительный)

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
Model Arguments
(Дополнительный)

Аргументы функции определяются в классе функции растра Python. Там перечисляются дополнительные параметры глубокого обучения и аргументы для экспериментов и улучшения, например, порог достоверности для настройки чувствительности. Названия аргументов заполняются при чтении модуля Python.

When you choose Single Shot Detector as the Model Type parameter value, the Model 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. 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 choose a pixel classification model such as Pyramid Scene Parsing Network, U-Net, or DeepLabv3 as the Model Type parameter value, the Model Arguments parameter will be populated with the following arguments:

  • use_net—Specifies whether 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 Pyramid Scene Parsing Network 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 Pyramid Scene Parsing Network 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—Contains the list of class values on which the model will not incur loss.

When you choose RetinaNet as the Model Type parameter value, the Model 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.

When you choose Multi Task Road Extractor or ConnectNet as the Model Type parameter value, the Model Arguments parameter will be populated with the following arguments:

  • gaussian_thresh—Sets the Gaussian threshold, which sets the required road width. The valid range is 0.0 to 1.0. The default is 0.76.
  • orient_bin_size—Sets the bin size for orientation angles. The default is 20.
  • orient_theta—Sets the width of orientation mask. The default is 8.
  • mtl_model—Sets the architecture type that will be used to create the model. Valid choices are linknet or hourglass for linknet-based or hourglass-based, respectively, neural architectures. The default is hourglass.

When you choose Image Captioner as the Model Type parameter value, the Model Arguments parameter will be populated with the following arguments:

  • decode_params—A dictionary that controls how the Image Captioner will run. The default value is {'embed_size':100, 'hidden_size':100, 'attention_size':100, 'teacher_forcing':1, 'dropout':0.1, 'pretrained_emb':False}.
  • chip_size—Sets the size of image to train the model. Images are cropped to the specified chip size. If image size is less than chip size, the image size is used. The default size is 224 pixels.
The decode_params, are comprised of the following six parameters:
  • embed_size—Sets the embedding size. The default is 100 layers in the neural network.
  • hidden_size—Sets the hidden layer size. The default is 100 layers in the neural network.
  • attention_size—Sets the intermediate attention layer size . The default is 100 layers in the neural network.
  • teacher_forcing—Sets the probability of teacher forcing. Teacher forcing is a strategy for training recurrent neural networks that uses model output from a prior time step as an input, instead of the previous output, during back propagation. Valid ranges is from 0.0 to 1.0. The default is 1.
  • dropout—Sets the dropout probability. Valid ranges is from 0.0 to 1.0. The default is 0.1.
  • pretrained_emb—Sets the pretrained embedding flag. If True, it will use fast text embedding. If False, it will not use the pretrained text embedding. The default is False.

When you choose Change Detector as the Model Type parameter value, the Model Arguments parameter will be populated with the following argument:

  • attention_type—Specifies the module type. The module choices are PAM (Pyramid Attention Module) or BAM (Basic Attention Module). The default is PAM.

All model types support the chip_size argument, which is the image chip size of the training samples. The image chip size is extracted from the .emd file from the folder specified in the Input Training Data parameter.

Value Table
Learning Rate
(Дополнительный)

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
(Дополнительный)

Specifies the preconfigured neural network that will be used as the architecture for training the new model. This method is known as Transfer Learning.

  • DenseNet-121 —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.
  • DenseNet-161 —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.
  • DenseNet-169 —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.
  • DenseNet-201 —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.
  • MobileNet version 2 —This preconfigured model will be trained on the ImageNet Database and is 54 layers deep geared toward Edge device computing, since it uses less memory.
  • ResNet-18 —The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than million images and is 18 layers deep.
  • ResNet-34 —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.
  • ResNet-50 —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.
  • ResNet-101 —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.
  • ResNet-152 —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.
  • VGG-11 —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.
  • VGG-11 with batch normalization —This preconfigured model will be 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.
  • VGG-13 —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.
  • VGG-13 with batch normalization —This preconfigured model will be 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.
  • VGG-16 —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.
  • VGG-16 with batch normalization —This preconfigured model will be 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.
  • VGG-19 —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.
  • VGG-19 with batch normalization —This preconfigured model will be 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.
  • DarkNet-53 —The preconfigured model will be a convolution neural network trained on the ImageNET Dataset that contains more than 1 million images and is 53 layers deep.
String
Pre-trained Model
(Дополнительный)

A pretrained model that will be used to fine-tune 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 %
(Дополнительный)

The percentage of training samples that will be used for validating the model. The default value is 10.

Double
Stop when model stops improving
(Дополнительный)

Specifies whether early stopping will be implemented.

  • Checked—Early stopping will be implemented, and the model training will stop when the model is no longer improving, regardless of the Max Epochs parameter value specified. This is the default.
  • Unchecked—Early stopping will not be implemented, and the model training will continue until the Max Epochs parameter value is reached.
Boolean
Freeze Model
(Дополнительный)

Specifies whether the backbone layers in the pretrained model will be frozen, so that the weights and biases remain as originally designed.

  • Checked—The backbone layers will be frozen, and the predefined weights and biases will not be altered in the Backbone Model parameter. This is the default.
  • Unchecked—The backbone layers will not be frozen, and the weights and biases of the Backbone Model parameter can be altered to fit the training samples. This takes more time to process but typically produces better results.

Boolean

Производные выходные данные

ПодписьОписаниеТип данных
Output Model

The output trained model file.

File

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

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
(Дополнительный)

Specifies the model type that will be used to train the deep learning model.

  • SSDThe 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.
  • UNETThe U-Net approach will be used to train the model. U-Net is used for pixel classification.
  • FEATURE_CLASSIFIERThe Feature Classifier approach will be used to train the model. This is used for object or image classification.
  • PSPNETThe Pyramid Scene Parsing Network (PSPNET) approach will be used to train the model. PSPNET is used for pixel classification.
  • RETINANETThe 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.
  • MASKRCNNThe MaskRCNN approach will be used to train the model. MaskRCNN is used for object detection. This approach is used for instance segmentation, which is precise delineation of objects in an image. This model type can be used to detect building footprints. 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.
  • YOLOV3The YOLOv3 approach will be used to train the model. YOLOv3 is used for object detection.
  • DEEPLABThe DeepLabV3 approach will be used to train the model. DeepLab is used for pixel classification.
  • FASTERRCNNThe FasterRCNN approach will be used to train the model. FasterRCNN is used for object detection.
  • BDCN_EDGEDETECTOR The Bi-Directional Cascade Network (BDCN) architecture will be used to train the model. The BDCN Edge Detector is used for pixel classification. This approach is useful to improve edge detection for objects at different scales.
  • HED_EDGEDETECTOR The Holistically-Nested Edge Detection (HED) architecture will be used to train the model. The HED Edge Detector is used for pixel classification. This approach is useful to in edge and object boundary detection.
  • MULTITASK_ROADEXTRACTOR The Multi Task Road Extractor architecture will be used to train the model. The Multi Task Road Extractor is used for pixel classification. This approach is useful for road network extraction from satellite imagery.
  • CONNECTNETThe ConnectNet architecture will be used to train the model. ConnectNet is used for pixel classification. This approach is useful for road network extraction from satellite imagery.
  • PIX2PIXThe Pix2Pix approach will be used to train the model. Pix2Pix is used for image to image translation. This approach creates a model object that generates images of one type to another. The input training data for this model type uses the Export Tiles metadata format.
  • CYCLEGANThe CycleGAN approach will be used to train the model. CycleGAN is used for image-to-image translation. This approach creates a model object that generates images of one type to another. This approach is unique in that the images to be trained do not need to overlap. The input training data for this model type uses the CycleGAN metadata format.
  • SUPERRESOLUTIONThe super-resolution approach will be used to train the model. Super-resolution is used for image-to-image translation. This approach creates a model object that increases the resolution and improves the quality of images. The input training data for this model type uses the Export Tiles metadata format.
  • CHANGEDETECTORThe Change Detector approach will be used to train the model. Change Detector is used for pixel classification. This approach creates a model object that uses two spatial-temporal images to create a classified raster of the change. The input training data for this model type uses the Classified Tiles metadata format.
  • IMAGECAPTIONERThe Image Captioner approach will be used to train the model. Image Captioner is used for image-to-text translation. This approach creates a model that generates text captions for an image.
String
batch_size
(Дополнительный)

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,...]
(Дополнительный)

Аргументы функции определяются в классе функции растра Python. Там перечисляются дополнительные параметры глубокого обучения и аргументы для экспериментов и улучшения, например, порог достоверности для настройки чувствительности. Названия аргументов заполняются при чтении модуля 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. 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 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:

  • 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—Contains the list of class values on which the model will not incur loss.

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].

When you choose MULTITASK_ROADEXTRACTOR or ConnectNet as the model_type parameter value, the arguments parameter will be populated with the following arguments:

  • gaussian_thresh—Sets the Gaussian threshold, which sets the required road width. The valid range is 0.0 to 1.0. The default is 0.76.
  • orient_bin_size—Sets the bin size for orientation angles. The default is 20.
  • orient_theta—Sets the width of orientation mask. The default is 8.
  • mtl_model—Sets the architecture type that will be used to create the model. Valid choices are linknet or hourglass for linknet-based or hourglass-based, respectively, neural architectures. The default is hourglass.

When you choose IMAGECAPTIONER as the model_type parameter value, the arguments parameter will be populated with the following arguments:

  • decode_params—A dictionary that controls how the Image Captioner will run. The default value is {'embed_size':100, 'hidden_size':100, 'attention_size':100, 'teacher_forcing':1, 'dropout':0.1, 'pretrained_emb':False}.
  • chip_size—Sets the size of image to train the model. Images are cropped to the specified chip size. If image size is less than chip size, the image size is used. The default size is 224 pixels.
The decode_params, are comprised of the following six parameters:
  • embed_size—Sets the embedding size. The default is 100 layers in the neural network.
  • hidden_size—Sets the hidden layer size. The default is 100 layers in the neural network.
  • attention_size—Sets the intermediate attention layer size . The default is 100 layers in the neural network.
  • teacher_forcing—Sets the probability of teacher forcing. Teacher forcing is a strategy for training recurrent neural networks that uses model output from a prior time step as an input, instead of the previous output, during back propagation. Valid ranges is from 0.0 to 1.0. The default is 1.
  • dropout—Sets the dropout probability. Valid ranges is from 0.0 to 1.0. The default is 0.1.
  • pretrained_emb—Sets the pretrained embedding flag. If True, it will use fast text embedding. If False, it will not use the pretrained text embeddings. The default is False.

When you choose CHANGEDETECTOR as the model_type parameter value, the arguments parameter will be populated with the following argument:

  • attention_type—Specifies the module type. The module choices are PAM (Pyramid Attention Module) or BAM (Basic Attention Module). The default is PAM.

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
(Дополнительный)

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
(Дополнительный)

Specifies the preconfigured neural network that will be used as the architecture for training the new model. This method is known as Transfer Learning.

  • DENSENET121The 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.
  • DENSENET161The 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.
  • DENSENET169The 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.
  • DENSENET201The 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.
  • MOBILENET_V2This preconfigured model will be trained on the ImageNet Database and is 54 layers deep geared toward Edge device computing, since it uses less memory.
  • RESNET18The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than million images and is 18 layers deep.
  • RESNET34The 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.
  • RESNET50The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than 1 million images and is 50 layers deep.
  • RESNET101The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than 1 million images and is 101 layers deep.
  • RESNET152The preconfigured model will be a residual network trained on the ImageNET Dataset that contains more than 1 million images and is 152 layers deep.
  • VGG11The 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.
  • VGG11_BNThis preconfigured model will be 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.
  • VGG13The 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.
  • VGG13_BNThis preconfigured model will be 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.
  • VGG16The 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.
  • VGG16_BNThis preconfigured model will be 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.
  • VGG19The 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.
  • VGG19_BNThis preconfigured model will be 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.
  • DARKNET53The preconfigured model will be a convolution neural network trained on the ImageNET Dataset that contains more than 1 million images and is 53 layers deep.
String
pretrained_model
(Дополнительный)

A pretrained model that will be used to fine-tune 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
(Дополнительный)

The percentage of training samples that will be used for validating the model. The default value is 10.

Double
stop_training
(Дополнительный)

Specifies whether early stopping will be implemented.

  • STOP_TRAININGEarly stopping will be implemented, and 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_TRAININGEarly stopping will not be implemented, and the model training will continue until the max_epochs parameter value is reached.
Boolean
freeze
(Дополнительный)

Specifies whether the backbone layers in the pretrained model will be frozen, so that the weights and biases remain as originally designed.

  • FREEZE_MODELThe backbone layers will be frozen, and the predefined weights and biases will not be altered in the backbone_model parameter. This is the default.
  • UNFREEZE_MODELThe backbone layers will not be frozen, and the weights and biases of the backbone_model parameter can be altered to fit the training samples. This takes more time to process but typically produces better results.
Boolean

Производные выходные данные

ИмяОписаниеТип данных
out_model_file

The output trained model file.

File

Пример кода

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)

Информация о лицензиях

  • Basic: Обязательно Image Analyst
  • Standard: Обязательно Image Analyst
  • Advanced: Обязательно Image Analyst

Связанные разделы