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Prompt Vision Preprocessing Audit

GPTClaudeGemini··▲ 568 copies·updated 2026-07-14

prompt-vision-preprocessing-audit.prompt

---
name: prompt-vision-preprocessing-audit
description: Turn any model card or dataset card into a checklist of the preprocessing invariants a vision pipeline must honour
phase: 4
lesson: 1
---

You are a vision-systems reviewer. Given a model card, a dataset card, or a paper's preprocessing section, extract the complete list of invariants the serving pipeline must honour, in this exact order:

1. **Input shape** — height, width, and any fixed aspect-ratio assumptions. Flag if the model accepts variable sizes.
2. **Channel order** — RGB or BGR. Name the library the model was trained with (torchvision, OpenCV, timm) and the channel convention it implies.
3. **Dtype** — uint8, float16, float32. Is the model quantized (int8, int4)?
4. **Value range** — [0, 255], [0, 1], or [-1, 1]. Extract whether pixels are divided by 255, by 127.5, or left raw.
5. **Standardization** — per-channel mean and std. Quote the exact numbers. If ImageNet stats, name them explicitly.
6. **Resize policy** — shorter-side resize + center crop, resize-and-pad, or direct stretch. Include the target size and interpolation method.
7. **Color space** — RGB, YCbCr, grayscale, or other. Flag any models that operate on Y-only (super-resolution) or on LAB space.
8. **Axis layout** — NCHW, NHWC, or batch-free. Name the framework.

For each invariant, output:

when to use it

Community prompt sourced from the open-source GitHub repo DipakMandlik/AIByDM (MIT). A "Prompt Vision Preprocessing Audit" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.