📄️ Main Module
The main entry point for the pytorch-segmentation-models-trainer CLI and core functionality.
📄️ Model Classes
This page is the API reference for all PyTorch Lightning model classes in pytorchsegmentationmodels_trainer.
📄️ Dataset Classes
This page is the API reference for all dataset classes in pytorchsegmentationmodels_trainer.
📄️ Loss Functions
This page is the API reference for all loss classes in pytorchsegmentationmodels_trainer.
📄️ Inference Processors
Inference processors orchestrate the full prediction pipeline for a trained model: reading a geospatial image, slicing it into tiles, running the model on each tile, merging the results back into a full-resolution output, and saving the inference to disk.
📄️ Custom Callbacks
Custom callbacks extend PyTorch Lightning's Callback and BasePredictionWriter interfaces to add visualisation, metrics reporting, loss normalisation, and polygonisation steps at specific points in the training or prediction loop.
📄️ Custom Optimizers
The project provides drop-in replacements for the standard PyTorch optimizers that add optional Gradient Centralization (GC) support. They are located in:
📄️ Mask Builder API
The Mask Builder system generates multi-channel training mask files from geospatial vector data (polygons) paired with raster images. For each input image it can produce up to six mask types, a CSV dataset manifest, and optionally bounding-box or polygon list files.