# Design exercise: Vision Models

> Classification, detection, segmentation, embeddings, vision transformers, data augmentation, and visual evaluation. Implement designOutline() returning non-empty values for: taskDefinition, representation, visualEval. Each value must name a concrete mechanism or decision.

Canonical: https://learn.significanthobbies.com/curriculum/drills/practice-vision-models

Classification, detection, segmentation, embeddings, vision transformers, data augmentation, and visual evaluation. Implement designOutline() returning non-empty values for: taskDefinition, representation, visualEval. Each value must name a concrete mechanism or decision.

## The mechanism

Vision systems convert pixels into task-specific spatial representations. Architecture, labels, augmentations, resolution, and evaluation must match deployment conditions.

## Expected evidence

A design outline with taskDefinition, representation, visualEval plus an explicit failure mode or trade-off.

## Hints

- Name the failure mode before selecting the mechanism.

## Sources and practice

- [Stanford CS231n](https://cs231n.stanford.edu/)
- [Study Vision Models](https://learn.significanthobbies.com/curriculum/concepts/vision-models)
- [Try the interactive drill](https://learn.significanthobbies.com/drills/practice-vision-models)
- [All practice briefs](https://learn.significanthobbies.com/curriculum/drills/)