Ship and explain the 3x3 plus-sign classifier

A shippable lab artifact includes: how to run it, the metric result (accuracy on a fixed four-image set; feature activations printed), the failure you can still reproduce (overfitting to one plus orientation, or leaking test grids into template design), the security gate for claiming 'computer vision' production readiness from 3×3 toys, and a rollback note. The user decision it supports remains: classify tiny binary images as plus vs not-plus with inspectable features.

report={'clean_accuracy':1.0,'flip_accuracy':0.89,'threshold':4,'limitations':['rotation','larger images']}
assert report['clean_accuracy']>=.9
print(report)

Expected evidence: stage evidence for mastery ship. Store this beside the fixture version so scores remain meaningful after content changes in computer-sees.

State operating limits for the 3x3 plus-sign classifier in plain language: fixture size, offline vs live dependencies, and what would require a new eval set. Shipping computer-sees is honest scoping, not maximal confidence language.

Fill this table in your notes for the 3x3 plus-sign classifier:

• Proved on plus, blank, and distractor 3×3 grids: …
• Unproved beyond the fixture: …
• Metric that blocks release: accuracy on a fixed four-image set; feature activations printed
• Failure still reproducible: overfitting to one plus orientation, or leaking test grids into template design
• Security gate: claiming 'computer vision' production readiness from 3×3 toys
• Rollback: …

Ship the narrative only when the unproved list is honest. Reviewers trust narrow claims that support classify tiny binary images as plus vs not-plus with inspectable features more than maximal language that collapses under the first production oddity.

Hand your ship note to a peer and ask them to recreate a proved/unproved ship note with rollback without watching you type. If they cannot, your evidence is still tribal knowledge. Tighten the run command and the metric line until a stranger can validate the 3x3 plus-sign classifier against plus, blank, and distractor 3×3 grids.

At the mastery and shipping stage for computer-sees, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about plus, blank, and distractor 3×3 grids that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: pixel-sum threshold accuracy on the same four images.

For this page specifically, success looks like a proved/unproved ship note with rollback while still centering the user decision to classify tiny binary images as plus vs not-plus with inspectable features. If you cannot point to a file, command, or assertion that proves that for the 3x3 plus-sign classifier, stay on this page instead of advancing.

Glossary: computer vision · Glossary: convolutional neural network

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