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name: model-building description: "Create and debug segmentation_models_pytorch model architectures, create_model calls, output shapes, auxiliary heads, and encoder freezing." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model Building

Use this sub-skill when the task asks to create, choose, inspect, or debug a segmentation_models_pytorch semantic segmentation model. SMP models are PyTorch torch.nn.Module objects that consume NCHW tensors and return segmentation masks, or (mask, label) when an auxiliary classification head is enabled.

Start Here

• Read API Reference for supported architecture keys, class names, constructor parameters, smp.create_model, output contracts, and encoder freeze helpers.
• Read Model Selection to choose between Unet, FPN, DeepLabV3Plus, Segformer, DPT, and the other SMP architectures.
• Read Troubleshooting when model construction, pretrained weights, tensor shapes, aux outputs, or DPT image sizes fail.
• Run model_smoke_test.py to create a model offline and verify a deterministic tiny forward pass.

Common Tasks

• Create by class: instantiate smp.Unet, smp.FPN, smp.DeepLabV3Plus, or another architecture class when the architecture is fixed in code.
• Create by name: use smp.create_model(arch="unet", encoder_name="resnet34", encoder_weights=None, in_channels=1, classes=1) for config-driven architecture selection.
• Configure channels/classes: set in_channels to the input tensor channel count and classes to the number of mask channels the model should emit.
• Enable aux output: pass aux_params={"pooling": "avg", "classes": label_count, "dropout": 0.5, "activation": "sigmoid"} and unpack mask, label = model(x).
• Freeze encoders: call model.freeze_encoder() before decoder-only fine-tuning and model.unfreeze_encoder() when full-model training should resume.
• Smoke check shapes: run the bundled script with encoder_weights=None by default before wiring the model into a training or export workflow.

Boundaries

• Encoder catalogs, encoder_name discovery, pretrained preprocessing functions, and input normalization belong in Encoders And Preprocessing.
• Losses, metrics, dataloaders, optimizer loops, and validation logic belong in Training And Evaluation.
• Checkpoint save/load, Hugging Face Hub loading, TorchScript, ONNX, tracing, and deployment export belong in Model Export.

Minimal Pattern

import torch
import segmentation_models_pytorch as smp

model = smp.create_model(
    arch="unet",
    encoder_name="resnet34",
    encoder_weights=None,
    in_channels=1,
    classes=1,
).eval()

x = torch.zeros(1, 1, 64, 64)
with torch.inference_mode():
    mask = model(x)
assert tuple(mask.shape) == (1, 1, 64, 64)