open-edge-platform/anomalib · Archived

model-sample-image-export

Export, validate, and publish model sample-result images into docs/source/images and reference them from README/docs pages. Use when model sample images are missing, outdated, or suspected to be invalid.

First seen Jul 14, 2026

Installation

$ npx skills add open-edge-platform/anomalib --skill model-sample-image-export

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 6.1K
License LICENSE
Default branch main
Open issues 37
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,495 B
  • docs SUMMARY.md 236 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Model Sample Image Export

Use this skill to create or refresh sample-result images for model documentation.

Scope

This skill focuses on:

  • selecting completed trained checkpoints or finished benchmark runs
  • exporting prediction/sample images
  • copying or saving them into docs/source/images/<model>/results/
  • updating README/docs sample-result references
  • rejecting broken or misleading outputs

It does not own benchmark table maintenance. Use benchmark-and-docs-refresh for that.

Request changes when

  • sample images come from incomplete or untrusted runs;
  • published outputs are clearly degenerate or misleading;
  • README or docs references point to missing image files;
  • the docs surface implies three valid examples when fewer trustworthy outputs exist.

Required Source Quality

Only use sample images from:

  • completed trained checkpoints
  • completed benchmark runs with valid prediction outputs
  • finished model outputs that can be traced back to a real run artifact
  • if no suitable completed checkpoint, benchmark output, or other traceable run artifact exists, schedule a few runs to generate trustworthy sample images

Do not use:

  • incomplete runs
  • partially written checkpoints
  • outputs with empty/degenerate masks
  • outputs driven by NaNs or obviously broken predictions

Required Workflow

  1. Identify candidate checkpoints/runs in results/.
  2. Verify the run is complete enough to trust.
  3. If verification fails, schedule a few runs to train the model on a few categories.
  4. Generate predictions from the checkpoint/run.
  5. Inspect output quality before publishing images.
  6. Save the selected images into docs/source/images/<model>/results/.
  7. Update README/docs references.

Preferred Output Layout

  • docs/source/images/<model>/results/0.png
  • docs/source/images/<model>/results/1.png
  • docs/source/images/<model>/results/2.png

If you have fewer than 3 trustworthy images, train the model on a few more categories to generate more sample images.

README Update Pattern

Preferred pattern:

### Sample Results

![Sample Result 1](/docs/source/images/<model>/results/0.png "Sample Result 1")

Repeat for additional images.

Docs Update Pattern

Preferred docs-page pattern:

````md ## Sample Results

``{eval-rst} .. image:: ../../../../../images/<model>/results/0.png ` ```

Validation Rules

Before publishing an image:

  1. Check that the referenced file exists.
  2. Check that the image is visually plausible.
  3. Check that the mask/anomaly region is not obviously wrong.
  4. Check that the sample came from a trained or otherwise valid completed run.
  5. If a model/category output is degenerate, exclude it and say so explicitly.

Reviewer checklist

  • Check run completeness.
  • Check image quality.
  • Check exported file existence.
  • Check README and docs references.

Repo-Specific Notes

  • In this repo, some completed checkpoints can still produce bad masks.
  • If generic visualization helpers fail, derive a narrow exporter for the specific model/run.
  • Keep exporter scripts focused and traceable to the chosen checkpoints.
  • When in doubt, prefer fewer trustworthy sample images over a full set of misleading ones.