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name: model-construction description: "Construct Keras segmentation models with Segmentation Models architectures, backbones, preprocessing, and input validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model Construction

Use this sub-skill when a task asks how to instantiate or diagnose Segmentation Models Keras models: choosing an architecture, selecting a backbone, configuring input_shape, classes, activation, encoder_weights, encoder_freeze, preprocessing, and constructor-specific validation.

Do not use this sub-skill for loss/metric math or full training loops. Route losses and metrics to losses-metrics; route optimizer/fit utilities, set_trainable, and set_regularization to training-utilities.

Operating checklist

1. Select the framework before import. In modern TensorFlow environments prefer TensorFlow Keras:

   import os
   os.environ["SM_FRAMEWORK"] = "tf.keras"  # or "keras"
   import segmentation_models as sm
   

   sm.set_framework("tf.keras") and sm.set_framework("keras") exist, but objects should be created only after the intended framework has been selected.

2. Choose task outputs. For binary segmentation use classes=1, activation="sigmoid". For mutually exclusive multiclass segmentation use a channel per class (often classes plus background) with activation="softmax". For independent multi-label masks use multiple channels with activation="sigmoid".

3. Choose architecture/backbone. See references/backbones-and-architectures.md for the supported names and selection guidance.

4. Configure preprocessing by backbone. Use preprocess_input = sm.get_preprocessing(backbone_name) and apply it to image tensors produced for the same backbone.

5. Choose input shape safely. Unet, Linknet, and FPN can use dynamic spatial dimensions such as (None, None, 3), but actual image height/width should normally be divisible by 32. PSPNet requires concrete height/width divisible by 6 * downsample_factor.

6. Avoid accidental network downloads. encoder_weights="imagenet" may download pretrained encoder weights and expects RGB-compatible input. For smoke tests, offline builds, grayscale, multispectral, or arbitrary channel counts, use encoder_weights=None or map non-RGB inputs to 3 channels before the model.

7. Smoke-check unusual configs. Use scripts/model_constructor_smoke.py for a safe constructor check; it defaults to encoder_weights=None and does not run prediction unless --predict is passed.

Key references

• references/api-reference.md — constructor signatures, defaults, framework and preprocessing examples.
• references/backbones-and-architectures.md — architecture/backbone trade-offs and supported names.
• references/troubleshooting.md — common constructor/import/shape failures and recoveries.
• scripts/model_constructor_smoke.py — CLI smoke script for offline model-construction checks.