Compose transforms in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - Compose transforms

Which metric matters for Compose transforms and WHY

Compose transforms are used to prepare data before training a model. The key metric to check here is data consistency and correctness. This means the transformed data should still represent the original information well, without errors or distortions.

For example, if you normalize images, the pixel values should be scaled correctly. If you flip or crop images, the labels should still match the content. So, metrics like data integrity checks or visual inspection are important.

In practice, after applying Compose transforms, you want to see if your model's training loss decreases and accuracy improves. This shows the transforms help the model learn better.

Confusion matrix or equivalent visualization

Compose transforms do not directly produce predictions, so no confusion matrix applies here.

Instead, you can visualize sample images before and after transforms to check correctness.

Original Image:  [Image of a cat]
After Compose:   [Flipped, normalized image of the same cat]
Label: Cat
    

Precision vs Recall tradeoff with concrete examples

Compose transforms affect data quality, which indirectly impacts precision and recall of the model.

If transforms are too aggressive (e.g., too much cropping), important features may be lost, causing the model to miss true positives (lower recall).

If transforms are too weak or inconsistent, the model may learn noise, causing false positives (lower precision).

Example: For a face detector, if Compose transforms randomly flip images but labels are not adjusted, the model may confuse left and right faces, hurting precision.

What "good" vs "bad" metric values look like for Compose transforms

Good Compose transforms lead to:

• Stable or improved training loss over epochs
• Improved validation accuracy
• Consistent data samples that match labels

Bad Compose transforms cause:

• Training loss that does not decrease or fluctuates wildly
• Validation accuracy that drops or is unstable
• Visual mismatch between transformed data and labels

Metrics pitfalls

• Data leakage: Applying transforms that use test data statistics can leak information and inflate metrics.
• Overfitting: Overly complex transforms may cause the model to memorize transformed data, hurting generalization.
• Incorrect label alignment: Some transforms (like flipping) require label changes; forgetting this causes wrong labels and poor metrics.
• Ignoring normalization: Not normalizing data properly can slow training and reduce accuracy.

Self-check question

Your model trained with Compose transforms has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why not?

Answer: No, it is not good. High accuracy can be misleading if the data is imbalanced (few fraud cases). The very low recall means the model misses most fraud cases, which is dangerous. You need to improve recall to catch more fraud, even if accuracy drops a bit.

Key Result

Compose transforms impact data quality, which affects model training loss and accuracy; correct transforms improve these metrics and maintain label consistency.