This is a PyTorch semantic segmentation library designed for building image masking frameworks. It provides a collection of over 500 pretrained convolutional and transformer-based encoders and various decoder architectures to perform binary and multiclass pixel-level classification.
The library features a modular backbone integration that decouples encoder choice from decoder logic. It supports custom input channel configurations and encoder depth tuning, allowing the modification of input layers to accept non-standard channel counts while preserving pretrained weights. Some configurations also allow for the attachment of auxiliary classification heads to produce both a segmentation mask and a global image label.
Additional capabilities include preprocessing functions aligned with pretrained encoder weights and tools for exporting trained models to the ONNX format for cross-platform deployment. The system also supports integration with model hubs for saving and loading weights.
