However, for sake of keeping thing simple, let us use only one image at a time. I'll let you figure out how you extract your images and make sure annotations are in proper format. Let us assume that you have to use these data augmentations in your training loop.
#Keras data augmentation rotation code#
The first file is going to contain the code for augmentations while the second file will contain the code for helper functions.īoth these files will live inside a folder called data_aug
We keep our code in 2 files, data_aug.py and bbox_util.py. I know a lot of datasets, and annotation tools store annotations in other formats, so, I'd leave it you to turn whatever storage format your data annotations are stored in, into the format described above.Īnd yes, for demonstration purposes we are going to use the following image of Lionel Messi scoring a beauty of a goal against Nigeria. Here, N represents the number of objects in the image, while the five columns represent:įormat for storing Bounding Box Annotations Format for Storing Annotationįor every image, we store the bounding box annotations in a numpy array with N rows and 5 columns. We will also go over some utility functions that will help us visualise detections, and some other stuff. This article will outline the general approach to writing an augmentation. Example Data Augmentation: Horizontal Flip In the stochastic one, the augmentation happens randomly, whereas in deterministic, the parameters of the augmentation (like the angle to be rotated are held fixed). We will also define a Data Augmentation that does nothing of it's own, but combines data augmentations so that they can be applied in a Sequence.įor each Data Augmentation, we will define two variants of it, a stochastic one and a deterministic one. We will define a uniform way to define these classes so that you can also write your own data augmentations. We will define our augmentations as classes, instances of which can be called to perform augmentation. We will be basing our little data augmentation library on Numpy and OpenCV. To be precise, here is the exact list of augmentations we will be covering.ĥ.
#Keras data augmentation rotation update#
It's this sort of data augmentation, or specifically, the detection equivalent of the major data augmentation techniques requiring us to update the bounding boxes, that we will cover in these article. Change of Bounding Boxes during Horizontal Flip However, doing the same augmentation for an object detection tasks also requires you to update the bounding box. For example, an augmentation which horizontally flips the image for classification tasks will like look the one above. However, the support for data augmentation for object detection tasks is still missing. Now, a lot of deep learning libraries like torchvision, keras, and specialised libraries on Github provide data augmentation for classification training tasks. Part 4: Baking augmentation into input pipelines Object Detection for Bounding Boxes Part 1: Basic Design and Horizontal FlippingĤ. The documentation for this project can be found by opening the docs/build/html/index.html in your browser or at this link.ġ. GitHub RepoĮverything from this article and the entire augmentation library can be found in the following Github Repo. This difference can be seen as noise being added to our data sample each time, and this noise forces the neural network to learn generalised features instead of overfitting on the dataset. Left: Original Image, Right: Augmented Image.Įach time the neural network sees the same image, it's a bit different due to the stochastic data augmentation being applied to it. This is especially true in case of online data augmentation, or augmenting every data sample stochastically each time we feed it to the training loop. As is the case with deep learning applications, the more data, the merrier.Īnother way to understand why data augmentation works so well is by thinking of it as added noise to our dataset. Why does Data Augmentation work?Ī very straightforward way to understand why data augmentation works is by thinking of it as a way to artificially expand our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems. Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. However, we may only have limited data with us.
When it comes to getting good performances from deep learning tasks, the more data the merrier.