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I am building a classification model. I want to use augmentation (less images + but no class imbalance)
I want to use rotation and translation. Does it matter what range I use and how big the range is?
Ex: Will there be a difference in my result if I use (assuming the number of rotations are the same).
A) +90 to -90 degrees B) +5 to -5 degrees
When using augmentation techniques like rotation and translation in a classification model, the range you choose can have an impact on the results. The range determines how much variation is introduced into the augmented images.
In the case of rotation, a larger range like +90 to -90 degrees means that the images can be rotated by a larger angle, allowing for more significant changes in the orientation of the objects in the image. This can be useful if the objects in your dataset exhibit a wide range of orientations.
On the other hand, a smaller range like +5 to -5 degrees limits the rotation to a smaller angle. This means the changes in orientation will be more subtle and may not capture as much variation in the data. This range might be appropriate if the objects in your dataset have a limited range of orientations.
The other potential impacts to consider are:
Variation in the data: Rotation and translation introduce variations in the images, which can help the model learn to recognize objects from different angles or positions. This can be particularly useful when there are limited images available, as it increases the diversity of the training data.
Generalization: Augmentation techniques like rotation and translation can improve the model's ability to generalize and make accurate predictions on new, unseen data. By exposing the model to augmented versions of the original images, it learns to handle different orientations and positions of objects, making it more robust.
Overfitting prevention: Augmentation helps prevent overfitting, which occurs when the model becomes too specialized in the training data and performs poorly on new data. By introducing variations through rotation and translation, the model is less likely to memorize specific examples and instead focuses on learning the underlying patterns and features that are relevant for classification.
