A Comprehensive Guide to Training Deep Learning Models in ArcGIS Pro
A Comprehensive Guide to Training Deep Learning Models in ArcGIS Pro
ArcGIS Pro offers robust capabilities for training deep learning models, enabling advanced geospatial analysis and image processing. This guide provides a detailed overview of the process, parameters, and available model architectures.
The core functionality centers around the “Train Deep Learning Model” tool. This tool requires input training data, organized into folders containing image chips, labels, and associated statistics. These data must be pre-processed using the “Export Training Data for Deep Learning” tool, ensuring consistent metadata formats (classified tiles, labeled tiles, multilabeled tiles, Pascal Visual Object Classes, RCNN masks, or CycleGAN) and band counts across all input folders. Multiple input folders are supported provided these conditions are met.
A key parameter is the “Model Type,” which dictates the underlying architecture used for training. ArcGIS Pro supports a wide range of pre-configured models, each optimized for specific tasks:
- Pixel Classification: BDCN Edge Detector, Change Detector, ClimaX, ConnectNet, DeepLabV3, HED Edge Detector, MMSegmentation, Multi Task Road Extractor, PSETAE, Pyramid Scene Parsing Network, and U-Net. These models are ideal for tasks such as land cover classification, change detection, and road network extraction.
- Object Detection: DETReg, FasterRCNN, MaskRCNN, MMDetection, RetinaNet, RTDetrV2, Single Shot Detector, and YOLOv3. These are suited for identifying and locating objects within imagery, such as buildings or vehicles.
- Object Tracking: Deep Sort and Siam Mask. These models track objects through video sequences.
- Image Translation: CycleGAN, Image Captioner, Pix2Pix, Pix2PixHD, and Super-resolution. These models perform tasks like image-to-image translation (e.g., converting satellite imagery to maps) and image enhancement (super-resolution).
- Panoptic Segmentation: MaX-DeepLab. This model performs both semantic segmentation (classifying each pixel) and instance segmentation (identifying individual objects).
Additional parameters offer fine-grained control over the training process. “Max Epochs” determines the number of training iterations. “Batch Size” controls the number of samples processed simultaneously, impacting performance and memory usage. “Learning Rate” adjusts the speed of model updates. The “Backbone Model” parameter allows for transfer learning, utilizing pre-trained models (ResNet, DenseNet, VGG, etc.) to accelerate training and improve accuracy. Pre-trained models can be specified using an Esri model definition file (.emd) or a deep learning package file (.dlpk). Fine-tuning is supported only for models trained using ArcGIS.
The tool also supports various optimization techniques. “Validation %” sets the proportion of data for model validation. “Stop when model stops improving” enables early stopping, preventing overfitting. “Freeze Model” allows freezing the backbone layers of a pre-trained model, preserving pre-trained weights. “Data Augmentation” artificially expands the training dataset, improving model robustness. This can be configured using default settings, no augmentation, or custom parameters for control over transformations like rotation, brightness, contrast, and zoom.
Advanced options include specifying “Chip Size” for image cropping, “Resize To” for image resizing, “Weight Initialization Scheme” for multispectral data handling, “Monitor Metric” (validation loss, average precision, accuracy, etc.) for checkpointing and early stopping, and “Enable Tensorboard” for real-time monitoring of the training process (supported for select model types).
The output of the tool is the trained deep learning model file, ready for deployment within ArcGIS Pro for further analysis.
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