segmentation_models.pytorch
A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
What is segmentation_models.pytorch?
Segmentation Models PyTorch (SMP) is a Python library that provides a high-level API for building and training semantic segmentation models using PyTorch. It solves the problem of complex model implementation by offering a wide selection of pretrained encoders and decoder architectures out of the box, significantly reducing development time for computer vision tasks.
Machine learning researchers, computer vision engineers, and data scientists working on image segmentation projects, from prototyping to production.
Developers choose SMP for its simplicity, extensive collection of pretrained backbones, and flexibility to mix-and-match architectures, enabling rapid experimentation and state-of-the-art performance without writing boilerplate code.
Overview
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
Use Cases
Best For
- Rapid prototyping of semantic segmentation models for research
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