Data Augmentation with Fastai Library
Data Augmentation
In AI and Machine Learning the prediction accuracy of the Deep Learning (Supervised) models is largely reliant on the amount and the diversity of data available during training. In simple terms, the amount of data required is proportional to the number of learnable parameters in the model. Data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Examples of common data augmentation techniques for images are rotation, flipping, perspective warping, brightness changes and contrast changes.
Data Augmentation using Fastai
Fastai has a method get_transforms() which applies default and random transformations with a probability of 75%: crop, horizontal flip, zoom up to 1.1, brightness and contrast, wrap (perspective).
Different Transform supported by Fastai
Brightness, contrast, crop, crop_pad, dihedral, dihedral_affine, flip_lr,skew, squish, zoom etc.
item_tfms and batch_tfms in Fastai
Item_tfms — Item transforms are pieces of code that run on each individual item, whether it be an image, category, or so forth. fastai includes many predefined transforms. one or several transforms applied to the items before batching them.
E.g item_tfms=Resize(128), item_tfms=RandomResizedCrop(128, min_scale=0.3)
Batch_tfms — one or several transforms applied to the batches once they are formed. Please find the example below which illustrates what happens when images are passed through batch_tfms.
Now that we know how to choose and use the Data Augmentation techniques in the fastai library, we can apply them to our training and validation images of our Deep Learning Neural network (eg, a UNET model) in order to train it.
Citations
https://medium.com/@pierre_guillou/data-augmentation-by-fastai-v1-84ca04bea302
