---

name: training-and-evaluation description: "Use for Anomalib's Engine fit/train/test/validate flow, metrics, callbacks, preprocessing, postprocessing, loggers, visualization, and training-mode guidance while keeping CLI and deployment internals out of scope." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Training and Evaluation

Use this sub-skill when the user needs to configure, explain, or debug Anomalib's training/evaluation loop, metric collection, callback order, preprocessing/postprocessing, experiment logging, or visualization.

Read first

• references/engine-and-metrics.md
• references/callbacks-and-logging.md
• references/troubleshooting.md
• scripts/training-modes.py
• scripts/evaluation-smoke.py

Safe operating boundary

Owns the core training/evaluation surface under:

• src/anomalib/engine
• src/anomalib/callbacks
• src/anomalib/metrics
• src/anomalib/pre_processing
• src/anomalib/post_processing
• src/anomalib/loggers
• src/anomalib/visualization

Covers:

• Engine.fit, Engine.train, Engine.validate, Engine.test, Engine.predict, Engine.from_config
• AnomalibModule.configure_callbacks
• get_callbacks
• Evaluator, AnomalibMetric, PostProcessor, PreProcessor, ImageVisualizer
• training-mode guidance such as barebones, checkpointing, and validation-first zero/few-shot behavior

Excludes:

• CLI parsing details
• export/deployment internals
• pipeline orchestration

Treat optional logger packages and GPU-only device checks as non-blocking.

Quick routing

User need	Do this
"How do I wire training, validation, and test?"	Read references/engine-and-metrics.md.
"Why do my callbacks or loggers behave strangely?"	Read references/callbacks-and-logging.md.
"Why are metrics missing, duplicated, or empty?"	Read references/troubleshooting.md.
"Can I run a CPU-only smoke for barebones mode?"	Run scripts/training-modes.py.
"Can I sanity-check metrics, preprocessing, postprocessing, and visualization?"	Run scripts/evaluation-smoke.py.

Operating notes

• Model components are usually chained as pre_processor -> model -> post_processor, with evaluator and visualizer attached as callbacks.
• Validation populates normalization and threshold state for PostProcessor and related metrics.
• The default evaluator uses prefixed image/pixel metrics so image-level and pixel-level results do not collide.
• Barebones mode removes Lightning logging overhead; use Anomalib's evaluator if you still need returned metrics.