Take Your Model and Accuracy to the Next Level With TTA and MIXUP

In this blog, we’ll explore two powerful technique Test-Time Augmentation (TTA) and Mixup that can help take your model’s performance to the next level. I’ll introduce these methods and demonstrate how to implement them effortlessly using Fast.ai(a library that simplifies and accelerates the process).

made by copilot

What is TTA

Test-Time Augmentation is a technique where multiple augmentations for example flips, rotations and crops are applied to a test sample, and predictions are generated for each augmented version. These predictions are then merged to produce a final prediction for the sample.

Why it works : augmenting the input at test time can help the model generalize better by considering multiple “perspectives” of the same data point, mitigating noise or biases that could affect a single inference.

Why you should use it

TTA increases robustness by exposing the model to multiple augmented versions of the test data during inference. (Robustness** means model’s ability to maintain its performance when faced with diffrent variations in the input data). A robust model can generalize well to unseen data, reducing its sensitivity to specific patterns. This Process also reduces Overfitting.Also boosts model’s accuracy** by giving more chances to predict during test time and finally combining those predictions .

When to not use TTA

TTA is Computationally expensive because it increases number of forward passes. Which can significantly increase inference time.

If the model is already robust , TTA might only offer minimal performance gains.

It is not suitable for autonomous driving since TTA increases latency.

What is Mixup

Mixup is a data augmentation technique where two training examples are combined by mixing them together. This creates a new training example that is a blend of the two.(For example :If you combine Image A (cat) and Image B (dog) with a 70:30 ratio, the new image looks like a blend of a cat and a dog. Its label would be 70% cat and 30% dog)

Why you should use it

Use Mixup when you want your model to generalize better to unseen data. It can make the model more tolerant of mislabeled training data.

When to not use mixup

Avoid Mixup if interpretability is critical, as mixed samples don’t represent real world data. It’s not ideal for tasks like object detection, where spatial relationships in the data are important. Also if your model is already robust you don’t need to use it since there will be no significant performance gain.

Code implementation

(you may access notebook from this link.)

You can use Mixup like this since Fast.ai makes it significantly easier you may just call function for your model training.

You can use TTA with just calling the .tta() like example below.

I highly encourage you to go to the notebook. (you may access notebook from this link.)