---

name: albumentations description: "Build, debug, serialize, and integrate Albumentations 2.x augmentation pipelines for images, masks, bboxes, keypoints, volumes, and PyTorch-style datasets." disable-model-invocation: true metadata: disco-role: operating license: MIT

Albumentations Repo Skill

Use this skill when a coding agent needs to work with Albumentations 2.x augmentation pipelines, choose transforms, validate targets, serialize/replay pipelines, or integrate augmentations into PyTorch-style data loading.

Albumentations is a NumPy/OpenCV-based augmentation library for computer vision and medical-imaging workflows. This skill covers the MIT-licensed albumentations package at version 2.0.8. The upstream README states that the original Albumentations repository is no longer maintained and points users to AlbumentationsX for active development; keep that maintenance status in mind when recommending new dependencies or migrations.

Quick Start

For deterministic/offline agent checks, suppress the package's import-time update check:

NO_ALBUMENTATIONS_UPDATE=1 python - <<'PY'
import albumentations as A
print(A.__version__)
PY

Minimal image pipeline:

import albumentations as A

transform = A.Compose(
    [
        A.Resize(256, 256),
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.2),
    ],
    strict=True,
    seed=137,
)
result = transform(image=image_np)
augmented = result["image"]

Install the base package for NumPy/OpenCV augmentation:

pip install albumentations

Install optional integrations only when needed:

pip install "albumentations[pytorch]"  # ToTensorV2 / ToTensor3D
pip install "albumentations[hub]"      # Hugging Face Hub helpers
pip install "albumentations[text]"     # text rendering helpers

Route By Task

• Use sub-skills/pipeline-composition/ to build or debug Compose, ReplayCompose, OneOf, SomeOf, RandomOrder, Sequential, OneOrOther, SelectiveChannelTransform, strict validation, additional targets, seeds, and pipeline operator edits.
• Use sub-skills/transform-catalog/ to choose transform families and parameters for pixel/color/noise/blur, crop/resize/pad/geometric, dropout, domain adaptation, spectrogram, text, and segmentation-safe pipelines.
• Use sub-skills/targets-and-formats/ to handle input/output keys, masks, bboxes, keypoints, labels, additional targets, multiple images, volumes, and 3D masks.
• Use sub-skills/serialization-and-reproducibility/ to save/load JSON or YAML configs, replay exact random choices, inspect applied parameters, handle custom Lambda objects, and reason about reproducibility.
• Use sub-skills/framework-integration/ for PyTorch-style Dataset integration, ToTensorV2, ToTensor3D, optional extras, tensor shapes, dtype/range expectations, and DataLoader worker seeding.

Shared References And Helpers

• references/installation-and-maintenance.md: package version, Python/dependency expectations, optional extras, maintenance status, and import/update-check behavior.
• references/troubleshooting.md: cross-cutting install/import, OpenCV, optional dependency, offline, validation, and migration troubleshooting.
• references/repo-provenance.md: source snapshot, evidence paths, and refresh baseline for staleness checks.
• scripts/albumentations_smoke_check.py: tiny safe import and pipeline smoke check for installed environments.

Common Decision Points

• Keep images as NumPy arrays in H,W,C until a final framework tensor transform; route tensor issues to framework-integration.
• Use strict=True while developing pipelines to catch unknown keys and invalid transform arguments early; route Compose-level validation to pipeline-composition.
• Declare bbox_params and keypoint_params on Compose whenever calls include bboxes or keypoints; route coordinate issues to targets-and-formats.
• For segmentation masks, prefer nearest-neighbor mask interpolation and explicit fill_mask; route transform support decisions to transform-catalog.
• For reproducible debugging, use seed, ReplayCompose, or save_applied_params=True depending on whether you need repeatable streams, exact replay, or sampled-parameter inspection.
• Do not assume optional PyTorch, Hub, or text helpers are available from a base install; check extras and imports first.

Safety And Self-Containment

This generated skill is self-contained. Runtime instructions, references, and helper scripts live inside this skill directory and do not require opening the original repository checkout. Treat original tests and source files as provenance evidence only; use the bundled sub-skills and helpers for future work.