EfficientNet scaling in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - EfficientNet scaling

Which metric matters for EfficientNet scaling and WHY

EfficientNet models balance accuracy and efficiency by scaling depth, width, and resolution together. The key metric to watch is Top-1 Accuracy on image classification tasks because it shows how well the model predicts the correct label. Also, FLOPs (floating point operations) and model size matter to measure efficiency. We want high accuracy with fewer FLOPs and smaller size, meaning the model is both accurate and fast.

Confusion matrix example for EfficientNet classification

      | Predicted Cat | Predicted Dog |
      |---------------|---------------|
      | True Cat: 90  | False Dog: 10 |
      | False Cat: 5  | True Dog: 95  |

      Total samples = 90 + 10 + 5 + 95 = 200

      Precision (Cat) = TP / (TP + FP) = 90 / (90 + 5) = 0.947
      Recall (Cat) = TP / (TP + FN) = 90 / (90 + 10) = 0.9
    

This matrix helps us understand how well EfficientNet distinguishes classes.

Precision vs Recall tradeoff with EfficientNet scaling

When scaling EfficientNet, increasing model size usually improves both precision and recall. But sometimes, a bigger model may catch more true positives (higher recall) but also more false positives (lower precision). For example, in a wildlife camera trap, high recall means finding all animals, but high precision means fewer false alarms. EfficientNet scaling tries to keep both high by balancing model complexity.

What good vs bad metric values look like for EfficientNet scaling

Good: Top-1 accuracy above 80% on ImageNet with moderate FLOPs (e.g., EfficientNet-B3). Precision and recall both above 85% show balanced performance.

Bad: Accuracy below 60% or very high FLOPs with little accuracy gain means inefficient scaling. Large gaps between precision and recall indicate the model is biased or missing classes.

Common pitfalls in metrics for EfficientNet scaling

• Accuracy paradox: High accuracy but poor recall on rare classes can mislead about model quality.
• Data leakage: If training and test images overlap, metrics look better but model won't generalize.
• Overfitting: Very high training accuracy but low test accuracy means scaling too much without enough data.
• Ignoring efficiency: Only looking at accuracy without FLOPs or size misses the point of EfficientNet scaling.

Self-check question

Your EfficientNet model has 98% accuracy but only 12% recall on a rare class like fraud detection. Is it good for production? Why or why not?

Answer: No, it is not good. High accuracy can be misleading if the rare class is missed often (low recall). For fraud, missing fraud cases is costly, so recall must be high even if accuracy is slightly lower.

Key Result

EfficientNet scaling aims for high accuracy with balanced precision and recall while keeping model size and FLOPs low for efficiency.