Model Card – One‑Page Quick Guide

27. Model Card – One‑Page Quick Guide#

27.1. What is the Purpose of a Model Card#

logo To give users a consistent, one‑glance explainer for any AI model you publish(classifier, detector, segmenter, & etc).

27.1.1. Example Model Card (Object Detector)#

27.1.2. Overview:#

This fish-finder model usually spots about 8 out of every 10 fish in a photo and only occasionally mistakes coral or rocks for fish. If you turn the confidence setting up, it will produce fewer false postives, but it might overlook a few more fish in the process.

27.1.3. Technical:#

YOLO11 Fish Detector – Grayscale reaches mean Average Precision (mAP) of 0.937 on the validation set, with precision 0.885 and recall 0.861 at the default confidence threshold (–conf 0.25). If you raise the threshold toward 0.8, precision climbs toward ~0.90, while recall falls proportionally (see PR-curve).

27.2. 1. Model Card Anatomy (60‑Second Walk‑Through)#

🔍 Section

What it tells you

Questions it answers

Model Details

Architecture, input size, training schedule

“Does it fit my task?”

Intended Use & Limitations

What it’s for / not for

“Will it work on my data?”

Dataset & Pre‑processing

Source, splits, augmentations

“Is training data representative and complete?”

Performance

Main metrics + curves

“How many False postives/False negatives?”

Ethical & Environmental Risks

Failure modes, bias notes

“Any concerns or red flags?”

Usage Examples

Code, CLI, thresholds

“How do I run the model?”

27.3. 2. Metrics Cheat‑Sheet (per task)#

Task

Key Metrics

FP/FN intuition

Classification

Accuracy, Precision/Recall, F1, ROC‑AUC

FP = wrong label; FN = missed class

Detection

mAP@IoU, Precision, Recall, F1

FP = extra box; FN = missed object

Segmentation

IoU / mIoU, Dice / F1, Precision, Recall

FP = extra pixels; FN = missed pixels

Example snippet: “At 0.25 confidence the detector scores mAP50 0.81, Precision 0.78, Recall 0.84 (≈ 22 FP & 16 FN per 100 predictions).”

27.4. 3. Choosing the Correct Model Threshold Setting#

Task

Typical thresholds

When to go lower

When to go higher

Classification

Logit > 0.5 (binary) / top‑k

Capture rare positives

Avoid false postives

Detection

--conf 0.2‑0.4 + IoU 0.5

Human‑in‑the‑loop review

Automated batch processing

Segmentation

Mask prob 0.5

Over‑segment for human review

Precise boundaries

Always validate on a slice of your data—lighting, turbidity, or domain shift moves the sweet spot.

27.5. 4. Standard FP/FN Reporting Template#

  1. Dataset snapshot

  2. Confusion matrix or counts table (per class)

  3. Curves – ROC / PR / mAP with chosen threshold marked

  4. Example gallery – top‑n FP & FN

  5. Version tag – e.g. v2.1.0, HF commit, Docker hash

27.6. 5. Training Facts (fill in)#

Item

Value

Model

🔷 <name / checkpoint>

Data

🔷 <dataset name + size>

Classes

🔷 <n>

Augmentations

🔷 <list>

Epochs / Batch

🔷 <values>

Best metric

🔷 <mAP / mIoU / F1>

27.7. 6. When Not to Use a Model#

Depends on data & task: Examples of failure mode:

  • Low‑light or turbidity > 2 m (underwater)

  • Camera tilt and/or movement > 45°

  • When Classes unseen in training set ( if the model hasn’t seen it then the model doesn’t know it)

27.8. 7. Maintenance & Versioning#

  • Semantic versioning – bump MAJOR if dataset or task changes.

  • Publish model card + changelog for every release.

  • Archive weights & data for reproducibility.

27.8.1. 📌 Copy‑Paste Summary (template)#

“Using <task‑specific threshold>, <MODEL_NAME> scores on the hold‑out set. Lowering threshold to <x> increases recall to <y> at the cost of precision. See model card for full FP/FN analysis.”

27.9. 0. Model‑at‑a‑Glance Diagram#

Pick the sketch that fits your task (or embed your own).

27.9.1. 🔹 Classification#

flowchart LR
    A[Input Image / Patch] --> B[Classifier]
    B --> C{Top‑k Labels + Scores}

27.9.2. 🔹 Object Detection (example)#

graph TD
    A[Input Image] --> B[Detector]
    B --> C[Boxes + Conf.]
    C --> D{Confidence Filter}
    D -->|≥ thr| E[Final Detections]
    D -->|< thr| F[Manual Review]

27.9.3. 🔹 Segmentation#

flowchart TD
    A[Input Image] --> B[Segmenter]
    B --> C[Per‑pixel Class Scores]
    C --> D{Mask Threshold}
    D -->|≥ thr| E[Binary/Color Masks]

Task: 🔷 <classification / detection / segmentation>
Classes: 🔷 <list or count>

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