---

name: segmentation-models description: "Use Segmentation Models for Keras/TensorFlow image segmentation model construction, losses, metrics, preprocessing, training utilities, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Segmentation Models Repo Skill

Use this repo skill when a task asks about the Python package segmentation_models / Segmentation Models: constructing Keras semantic-segmentation models, selecting backbones, configuring preprocessing, choosing segmentation losses/metrics, or assembling small training/evaluation/fine-tuning workflows.

This skill is for using the package as an operating library. If the task is about editing the repository source, packaging releases, or changing tests, use a Python repository maintenance skill instead.

Install and import baseline

Segmentation Models 1.x expects a Keras backend plus its own package dependencies. The package metadata installs keras_applications, image-classifiers, and efficientnet; TensorFlow/Keras itself is chosen separately for the target platform.

pip install segmentation-models
# Install a compatible TensorFlow/Keras backend for the environment.

In modern TensorFlow environments, prefer TensorFlow Keras and set the framework before import:

import os
os.environ.setdefault("SM_FRAMEWORK", "tf.keras")
import segmentation_models as sm

print(sm.__version__)
print(sm.framework())
print(sm.get_available_backbone_names()[:5])

Run scripts/check_environment.py when a task needs a no-data import/framework/backbone smoke check. Add --build-model only when the environment can import TensorFlow/Keras and can build a tiny offline model with encoder_weights=None.

Route map

User task	Read
Instantiate Unet, Linknet, FPN, or PSPNet; choose backbone_name, input_shape, classes, activation, encoder_weights; debug shape/backbone/framework constructor errors.	sub-skills/model-construction/SKILL.md
Choose IOUScore, FScore, Dice/Jaccard/focal/CE losses; configure class weights/indexes, thresholds, per-image reduction; hand-check metric math.	sub-skills/losses-metrics/SKILL.md
Assemble training/evaluation/fine-tuning loops; use get_preprocessing; set image/mask shapes; handle non-RGB data; freeze/unfreeze encoders; add regularization; run synthetic training smoke checks.	sub-skills/training-utilities/SKILL.md
Diagnose package-wide install/import/backend/version problems before a workflow-specific route.	references/troubleshooting.md
Decide whether this skill is current for a checkout or package release.	references/repo-provenance.md

Fast workflow patterns

Binary segmentation model

import os
os.environ.setdefault("SM_FRAMEWORK", "tf.keras")
import segmentation_models as sm

BACKBONE = "resnet34"
preprocess_input = sm.get_preprocessing(BACKBONE)
model = sm.Unet(
    BACKBONE,
    classes=1,
    activation="sigmoid",
    encoder_weights="imagenet",
)
model.compile("Adam", loss=sm.losses.bce_jaccard_loss, metrics=[sm.metrics.iou_score])

Multiclass model

model = sm.FPN(
    "efficientnetb0",
    input_shape=(256, 256, 3),
    classes=4,
    activation="softmax",
    encoder_weights="imagenet",
)
model.compile("Adam", loss=sm.losses.cce_jaccard_loss, metrics=[sm.metrics.IOUScore()])

Offline smoke construction

model = sm.Unet(
    "resnet18",
    input_shape=(32, 32, 3),
    classes=1,
    activation="sigmoid",
    encoder_weights=None,
)

Safety and scope notes

• encoder_weights="imagenet" may download weights and expects RGB-compatible input. Use encoder_weights=None for offline checks, non-RGB direct inputs, and deterministic smoke tests.
• The bundled scripts use synthetic data or parser/help checks only; they do not clone datasets, train benchmark models, or require GPU.
• CUDA/GPU is optional acceleration for real training in this package. CPU TensorFlow/Keras is sufficient for import, model-construction, loss/metric, and tiny training validation.
• Original notebooks and tests were used as evidence, but runtime guidance here is self-contained; do not require future agents to open or execute files from the original checkout.