segmentation_models.pytorch

Why we included this project

Semantic segmentation in PyTorch usually means wiring together an encoder, a decoder, and a handful of loss functions before you can even test an idea. This library collapses most of that setup into a few lines: pick an architecture like U-Net, DeepLabV3+, or Segformer, choose one of the 800-plus pretrained convolutional or transformer encoders (including timm backbones), and you have a working model ready for training. It also ships with the losses and metrics that actually matter for segmentation work, so you are not hunting down separate helper packages for Dice or Jaccard scores. Since everything is a standard PyTorch module, the models drop straight into existing training loops, and ONNX export or tracing is available when you need to ship them. That makes it a practical bridge between a quick prototype and a production segmentation service.