segmentation_models.pytorch: a high-level PyTorch library for image semantic segmentation with over 800 pretrained encoders

What it solves

This library provides a streamlined way to implement image semantic segmentation, removing the need to manually build complex encoder-decoder architectures from scratch. It simplifies the process of choosing, initializing, and training neural networks that can identify and outline specific objects within an image.

How it works

The library acts as a high-level wrapper around PyTorch, allowing users to create segmentation models by combining a pretrained encoder (backbone) with a specific decoder architecture. It extracts intermediate features from the encoder and feeds them into the decoder to produce a segmentation mask. It also includes built-in support for popular segmentation metrics and loss functions (such as Dice and Jaccard) and supports ONNX export for deployment.

Who it’s for

It is designed for developers and researchers working on computer vision tasks, specifically those needing to perform binary or multiclass image segmentation for applications like background removal or medical imaging.

Highlights

• Extensive Model Library: Supports 12 different encoder-decoder architectures, including Unet, Unet++, Segformer, and DeepLabV3+.
• Massive Encoder Selection: Offers over 800 pretrained encoders, including support for the timm library.
• Easy Integration: A high-level API allows for the creation of a full neural network in just two lines of code.
• Flexible Configuration: Supports custom input channels (e.g., for grayscale images) and optional auxiliary classification outputs.
• Deployment Ready: Compatible with torch script, trace, compile, and ONNX export.