Each copy, however, is different from the other in certain aspects depending on the augmentation techniques you apply like shifting, rotating, flipping, etc.Īpplying these small amounts of variations on the original image does not change its target class but only provides a new perspective of capturing the object in real life. Image augmentation is a technique of applying different transformations to original images which results in multiple transformed copies of the same image. Model building with Keras ImageDataGenerator.Image Augmentation techniques with Keras ImageDataGenerator.Understanding and coding Neural Networks From Scratch in Python and R.If not, I suggest going through the following resources first: I am assuming that you are already familiar with neural networks. This will not only make your model robust but will also save up on the overhead memory! Now let’s dive deeper and check out the different ways in which this class is so great for image augmentation. Keras ImageDataGenerator is a gem! It lets you augment your images in real-time while your model is still training! You can apply any random transformations on each training image as it is passed to the model. I have got to admit, I used to do this until I stumbled upon the ImageDataGenerator class. augmenting images and storing them in a numpy array or in a folder. But many people use the conservative way of augmenting the images i.e. You can come up with new transformed images from your original dataset. The image augmentation technique is a great way to expand the size of your dataset. It was in times like these when I came across the concept of image augmentation. When working with deep learning models, I have often found myself in a peculiar situation when there is not much data to train my model. Learn Image Augmentation using Keras ImageDataGenerator.
0 Comments
Leave a Reply. |