A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
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.
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
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Integrates over 800 pretrained convolution- and transformer-based backbones, including timm support, allowing rapid model initialization with ImageNet weights for faster convergence.
Offers a consistent interface across 12 encoder-decoder models like Unet, Segformer, and DPT, enabling easy swapping and experimentation with minimal code changes.
Includes popular segmentation metrics and losses such as Dice, Jaccard, and Tversky, reducing the need for custom implementation in training routines.
Supports ONNX export and compatibility with torch script, trace, and compile, facilitating smooth deployment in production environments without major modifications.
Lacks built-in data loading, augmentation, or training loops; users must implement these separately, adding complexity for end-to-end workflow setup.
With hundreds of encoders, selection can be daunting without clear guidance, potentially leading to suboptimal model performance without expert knowledge.
Some architectures lack pretrained checkpoints (e.g., Unet, PSPNet), requiring users to train from scratch or source external weights, increasing time and resource investment.