anomalib-benchmarking
anomalib-benchmarking is a Model training & evaluation skill for AI agents, published by open-edge-platform in the anomalib repository on GitHub. Skillspace rates anomalib-benchmarking D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV. Use when comparing multiple models/datasets/categories in one sweep, or authoring/editing a benchmark config YAML. Do not use for training a single model (see anomalib-training) or the tiled-ensemble pipeline (see anomalib-tiled-ensemble). For turning measured results into README/docs benchmark tables, see the benchmark-and-docs-refresh skill.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 6,152
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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