Why architecture design impacts performance in Computer Vision - Why Metrics Matter

Metrics & Evaluation - Why architecture design impacts performance

Which metric matters and WHY

In computer vision, architecture design affects how well a model learns and predicts. Key metrics include accuracy for overall correctness, precision and recall for class-specific performance, and F1 score to balance precision and recall. These metrics show if the architecture extracts useful features and generalizes well.

Confusion matrix example

      Predicted
      | Cat | Dog |
    ---+-----+-----+
    Cat| 50  | 10  |
    Dog| 5   | 35  |

    TP (Cat) = 50, FP (Cat) = 10, FN (Cat) = 5, TN (Cat) = 35
    

This matrix helps calculate precision and recall for each class, showing how architecture impacts correct and wrong predictions.

Precision vs Recall tradeoff

A complex architecture might improve recall by finding more true objects but lower precision by adding false detections. A simpler design might have high precision but miss some objects (low recall). Choosing architecture depends on whether missing objects or false alarms are worse.

Example: In face recognition, high precision avoids false matches, but in medical image detection, high recall avoids missing diseases.

Good vs Bad metric values

Good: Accuracy above 90%, precision and recall balanced above 85%, F1 score high. This means the architecture captures features well and predicts reliably.

Bad: Accuracy high but recall very low (e.g., 40%), or precision very low. This shows the architecture misses many true cases or makes many false alarms, hurting performance.

Common pitfalls

• Overfitting: Complex architectures may memorize training data, showing high accuracy but poor real-world results.
• Data leakage: If test data leaks into training, metrics look falsely good, hiding architecture flaws.
• Ignoring class imbalance: Accuracy can be misleading if one class dominates; precision and recall give clearer insight.

Self-check question

Your model has 98% accuracy but only 12% recall on detecting a rare object. Is it good for production?

Answer: No. The model misses most true objects (low recall), so it fails its purpose despite high accuracy. The architecture likely does not capture important features for that object.

Key Result

Architecture design impacts key metrics like precision, recall, and F1 score, which reveal how well the model learns and generalizes.