Editing Computer Vision (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Computer Vision Architecture Trade-offs
! Architecture !! Speed !! Accuracy !! Data Efficiency !! Interpretability
|-
| Simple CNN || Fast || Moderate || Moderate || Low
|-
| ResNet-50 || Moderate || High || Good || Low
|-
| EfficientNet || Fast || Very high || Good || Low
|-
| Vision Transformer (ViT) || Slow || Very high || Low (needs large data) || Moderate (attention maps)
|-
| YOLO (real-time detection) || Very fast || High || Moderate || Low
|}

'''Failure modes and edge cases:'''
* '''Distribution shift''' — A model trained on daytime images may fail catastrophically at night or in fog. Always evaluate on data matching deployment conditions.
* '''Adversarial examples''' — Imperceptible pixel perturbations can cause confident misclassification. Critical in security contexts.
* '''Class imbalance''' — Rare but important classes (e.g., a specific disease) are underrepresented. Use weighted loss, oversampling, or focal loss.
* '''Shortcut learning''' — Models learn spurious correlations (e.g., "cows are in green fields") rather than the actual discriminative features.
* '''Label noise''' — Incorrect annotations in training data systematically bias learning. Perform label quality audits on a random sample.
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