Online and offline data augmentation are two strategies used to enhance the training dataset for machine learning models, particularly in the field of computer vision. The main difference between the two lies in when and how augmentation is applied. In offline data augmentation, the original dataset is augmented ahead of time, generating a new dataset that includes both the original and transformed images. This expanded dataset is then used for training the model. In contrast, online data augmentation applies transformations in real-time during the training process. That means every time a model accesses an image, it can receive a different version of that image based on specified augmentation techniques.
For example, offline data augmentation might involve creating multiple versions of an image by rotating, scaling, or applying color changes, and saving these versions to disk. This newly created dataset can include thousands or millions of augmented examples. Because this augmentation happens beforehand, it tends to use more storage space and requires additional time to generate these files. Developers can experiment with various transformations, selecting the most effective configurations during the preprocessing stage.
On the other hand, online data augmentation is often implemented using libraries that modify images on-the-fly as they are being loaded into the model during training. This method can be more efficient in terms of disk space since transformations are not saved permanently, and different training epochs can use various versions of the same images, which helps prevent overfitting. For instance, while training, a single image might be randomly flipped or adjusted in brightness each time it is fetched, thereby introducing variability. This way, the model can generalize better while requiring less storage space. However, online augmentation may introduce some computational overhead since the transformations are applied in real-time during training.