nn.Conv2d layers in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - nn.Conv2d layers

Which metric matters for nn.Conv2d layers and WHY

When using nn.Conv2d layers in neural networks, the main goal is to learn useful features from images. The key metrics to check are training loss and validation accuracy. Loss tells us how well the model fits the training data, while accuracy shows how well it predicts new images. We also watch overfitting signs by comparing training and validation metrics. For tasks like image classification, accuracy is important. For image generation or segmentation, other metrics like Intersection over Union (IoU) or Mean Squared Error (MSE) matter. But overall, loss and accuracy guide us to know if the convolution layers are helping the model learn useful patterns.

Confusion matrix example for image classification with nn.Conv2d

      | Predicted Cat | Predicted Dog |
      |---------------|---------------|
      | True Cat: 50  | False Dog: 5  |
      | False Cat: 3  | True Dog: 42  |

      Total samples = 50 + 5 + 3 + 42 = 100

      Precision (Cat) = TP / (TP + FP) = 50 / (50 + 5) = 0.909
      Recall (Cat) = TP / (TP + FN) = 50 / (50 + 3) = 0.943

      Precision (Dog) = 42 / (42 + 3) = 0.933
      Recall (Dog) = 42 / (42 + 5) = 0.893
    

This confusion matrix shows how well the convolutional model classifies cats and dogs. Precision and recall help us understand errors in each class.

Precision vs Recall tradeoff with nn.Conv2d models

Imagine a model using nn.Conv2d layers to detect defects in product images.

• High precision means most detected defects are real defects. This avoids wasting time fixing false alarms.
• High recall means the model finds most real defects, even if some false alarms happen.

If the factory wants to avoid missing any defect, recall is more important. But if fixing false alarms is costly, precision matters more. The convolutional layers help extract features, but tuning the model affects this tradeoff.

Good vs Bad metric values for nn.Conv2d layers in image classification

Good metrics:

• Training loss steadily decreases and validation loss decreases or stabilizes.
• Validation accuracy above 80% for simple datasets like CIFAR-10.
• Balanced precision and recall above 0.8 for each class.

Bad metrics:

• Training loss decreases but validation loss increases (overfitting).
• Validation accuracy stuck near random guess (e.g., 10% for 10 classes).
• Very low recall or precision for important classes.

Common pitfalls when evaluating nn.Conv2d models

• Accuracy paradox: High accuracy can be misleading if classes are imbalanced. For example, 90% accuracy if 90% of images are one class.
• Data leakage: If test images leak into training, metrics look unrealistically good.
• Overfitting: Model memorizes training images but fails on new images, seen by gap between training and validation metrics.
• Ignoring class imbalance: Not using precision and recall can hide poor performance on rare classes.

Self-check question

Your convolutional model has 98% training accuracy but only 12% recall on the defect class in validation. Is it good for production? Why or why not?

Answer: No, it is not good. The model is very good at training data but misses most defects in new images (low recall). This means it fails to find important defects, which is risky. The model likely overfits and needs better training or data.

Key Result

For nn.Conv2d layers, monitoring training loss and validation accuracy with precision and recall per class ensures the model learns useful image features without overfitting.