qubvel-org/segmentation_models.pytorch

Health v2 · maintenance only

Adoption not part of the score

11706 stars · 1841 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library providing neural networks for image semantic segmentation with a simple high-level API. It includes 12 encoder-decoder architectures (Unet, Unet++, DeepLabV3+, Segformer, DPT, etc.) and 800+ pretrained convolutional and transformer-based encoders, plus common losses and metrics.

Use cases

• train a semantic segmentation model in pytorch
• segment images with a pretrained unet
• fine-tune a segmentation model with pretrained encoders
• use segformer or deeplabv3+ for image segmentation
• export a segmentation model to onnx
• use dice or jaccard loss for segmentation training

When to choose

• you need ready-made segmentation architectures with pretrained backbones in PyTorch
• you want a minimal two-line API to build segmentation networks
• you need timm encoder support and ONNX/TorchScript export

When to avoid

• you need object detection or classification rather than pixel-level segmentation
• you work outside PyTorch (e.g., TensorFlow or JAX)
• you need fully custom architectures not covered by the 12 provided models

Facets

library · maturity stable

machine-learning deep-learning image-processing computer-vision computer-vision deep-learning machine-learning image-processing python cross-platform semantic-segmentation pytorch unet pretrained-encoders timm onnx-export segmentation-models encoder-decoder gpu

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