Define the 3x3 plus-sign classifier input contract

The accepted input remains: 3×3 binary grids and a hand-built feature or weight pattern. Keep parsing and normalization in functions that do not score, train, or call a model. That split lets a test fail the boundary without blaming the core logic. The user-facing decision stays: classify tiny binary images as plus vs not-plus with inspectable features.

def validate(img):
 if len(img)!=3 or any(len(r)!=3 for r in img): raise ValueError('expected 3x3')
 if any(p not in (0,1) for r in img for p in r): raise ValueError('binary pixels only')
 return img
print(validate([[0,1,0],[1,1,1],[0,1,0]]))

Expected evidence: the validated 3×3 grid. If the contract is silent on a bad value, later debugging will look like an algorithm bug when it is really a data bug.

Write one assertion for a neighboring valid input to the 3x3 plus-sign classifier so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of computer-sees—not as comments you plan to delete.

List every field in plus, blank, and distractor 3×3 grids and mark each as required, optional, or forbidden. Required fields must fail loudly when missing; optional fields need defaults you can quote in a test; forbidden fields (secrets, raw PII, path escapes) must never be accepted silently. This list is the contract for the 3x3 plus-sign classifier.

Add one sentence about encoding, units, or timezones if relevant to 3×3 binary grids and a hand-built feature or weight pattern. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to classify tiny binary images as plus vs not-plus with inspectable features.

Write the error string you want for the most likely bad input. Prefer ValueError('threshold out of range')-style messages over generic invalid input. The contract’s job is to make overfitting to one plus orientation, or leaking test grids into template design harder to confuse with a model or algorithm bug later.

At the data contract 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 malformed inputs rejected with field-named errors 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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