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Model Sample Image Export

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model-sample-image-export

Model Sample Image Export is an Agent Skill for documents published in the open-edge-platform/anomalib repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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 Use when model sample images are missing, outdated, or suspected to be invalid.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/open-edge-platform/anomalib/blob/HEAD/.agents/skills/model-sample-image-export/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/model-sample-image-export, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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:

    ## 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.