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name: segmentation-models-pytorch description: "Route segmentation_models_pytorch tasks for semantic segmentation model creation, encoders, preprocessing, losses, metrics, export, and repository maintenance." disable-model-invocation: true metadata: disco-role: operating license: MIT

Segmentation Models PyTorch

Use this repo skill when the task involves segmentation_models_pytorch (SMP), a PyTorch semantic segmentation library with high-level model constructors, pretrained encoders, segmentation losses/metrics, and save/load/export workflows.

Start Here

• Read Package Overview for the public API surfaces, dependency expectations, and how the sub-skills fit together.
• Read Repository Provenance before deciding whether this skill is current for a checkout or package version.
• Read Troubleshooting for cross-cutting install/import, PyTorch, Hugging Face Hub, optional dependency, and routing failures.
• Run check_install.py when an environment may not have SMP, PyTorch, timm, Hugging Face Hub, or CUDA visibility set up correctly.

Route By Task

• Use model-building to create or debug Unet, FPN, DeepLabV3Plus, Segformer, DPT, smp.create_model, tensor shapes, auxiliary heads, or encoder freeze/unfreeze behavior.
• Use encoders-preprocessing to choose encoder_name, list supported encoders, migrate deprecated timm- names, use tu- timm universal encoders, configure pretrained weights, or get preprocessing mean/std functions.
• Use training-evaluation for DiceLoss, JaccardLoss, TverskyLoss, FocalLoss, binary/multiclass/multilabel target shapes, smp.metrics.get_stats, IoU/F1/accuracy, or safe training/evaluation loops.
• Use model-export for save_pretrained, smp.from_pretrained, changed class counts with strict=False, Hugging Face Hub sharing, ONNX, TorchScript, torch.export, or torch.compile readiness.
• Use repo-development only when the user is editing an SMP checkout: adding models/encoders/losses, updating docs tables, selecting focused tests, or maintaining package metadata.

Quick Install And Import Check

SMP is published as segmentation-models-pytorch on PyPI and imports as segmentation_models_pytorch:

pip install -U segmentation-models-pytorch
python - <<'PY'
import segmentation_models_pytorch as smp
print(smp.__version__)
print(sorted(smp.MODEL_ARCHITECTURES_MAPPING))
PY

For local repo work, install the current checkout in editable mode with its runtime dependencies. Add test or docs extras only when the task actually needs tests or documentation builds.

Common Public APIs

• Model constructors: smp.Unet, smp.UnetPlusPlus, smp.MAnet, smp.Linknet, smp.FPN, smp.PSPNet, smp.PAN, smp.DeepLabV3, smp.DeepLabV3Plus, smp.UPerNet, smp.Segformer, smp.DPT.
• Generic model factory: smp.create_model(arch, encoder_name="resnet34", encoder_weights="imagenet", in_channels=3, classes=1, **kwargs).
• Encoder utilities: smp.encoders.get_encoder_names(), get_encoder(...), get_preprocessing_params(...), and get_preprocessing_fn(...).
• Losses and modes: smp.losses.BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE, plus Dice, Jaccard, Tversky, Focal, Lovasz, SoftBCE, SoftCE, and MCC losses.
• Metrics: smp.metrics.get_stats(...) followed by iou_score, f1_score, accuracy, precision, recall, or related rates.
• Persistence: model.save_pretrained(...) and smp.from_pretrained(...) for local directories or Hub repos when network and credentials are available.

Safe Defaults

• Prefer encoder_weights=None for offline smoke tests to avoid downloading pretrained weights.
• Use small tensor sizes such as 64x64 or 128x128 for shape checks; do not run notebook-scale training or dataset downloads unless the user explicitly requests them.
• Use classes=1 for binary logits, classes=N for multiclass/multilabel logits, and choose the matching loss/metric mode in training-evaluation.
• Treat Hugging Face Hub pushes, pretrained checkpoint downloads, ONNX export, and long training examples as optional/networked workflows with explicit prerequisites.

When To Refresh This Skill

Refresh this skill when SMP changes model constructors, architecture registry names, encoder registry behavior, pretrained weight handling, loss/metric signatures, save/load APIs, export compatibility, package dependencies, or maintainer test conventions. Use Repository Provenance to compare the current checkout against the generation baseline.