Deep learning models

Disponible con licencia de Image Analyst.

The following table provides an overview of the deep learning models available in ArcGIS Pro. Each row provides compatible metadata formats and the main use of the specific model. Where available, accompanying examples are included.

Deep learning model type	Supported metadata	Task	Example
BDCN Edge Detector	Classified tiles	Pixel classification	Land parcel extraction
Change Dectector	Classified tiles Change detection	Pixel classification (change detection)	Detect building change
ConnectNet	Classified tiles	Pixel classification
CycleGAN	Export tiles CycleGAN	Image translation (unpaired images)	SAR to RGB translation
DeepLab	Classified tiles	Pixel classification
Faster RCNN	PASCAL_VOC_rectangles KITTI_rectangles	Object detection
Feature classifier	Labeled tiles Imagenet Multi-labeled tiles	Object detection	Feature categorization
HED Edge Detector	Classified tiles	Pixel classification	Land parcel extraction
Image Captioner	Image captioning	Image translation
Mask RCNN	RCNN masks	Object detection (instance segmentation)	Caribou detection and classification
MMDetection	PASCAL_VOC_rectangles KITTI_rectangles	Object detection
MMSegentation	Classified tiles	Pixel classification
Multi-Task Road Extractor	Classified tiles	Pixel classification	Automatic road extraction
Pix2Pix	Export tiles	Image translation (paired imges)	Colorizing historic imagery
PSPNet	Classified tiles	Pixel classification
RetinaNet	PASCAL_VOC_rectangles KITTI_rectangles	Object detection	Utility and vegetation detection
Siam Mask	RCNN Masks	Object tracker
SSD	PASCAL_VOC_rectangles KITTI_rectangles	Object detection	Detect palm tree health
Super-resolution	Super-resolution	Image translation	Increase image resolution
U-Net	Classified tiles	Pixel classification	Extracting building footprints
YOLOv3	PASCAL_VOC_rectangles KITTI_rectangles	Object detection

Nota:

Some of the examples that use the Python Notebook for training can be performed using the Train Deep Learning Model tool.