With the help of image augmentation you can improve the quality of your neuronal networks by a fair amount. In this post we explain everything you need to know about image augmentation and how you can use it to improve your neuronal networks quality.
We will cover the following topics:
- What means image augmentation?
- Why is it a good idea to augment your images?
- What are augmentation pipelines and how can you use them effctively?
What means image augmentation?
In the space of deep learning image augmentation is describing the process create artifical images based on your original images. For example you can rotate an image or apply a greyscale filter to change to original image. This process can be automated with special software and starts after the original image data was labeled. This means the new generated images dont need to be labeled all over again but are already labeled based off the original image.
Why is it a good idea to augment your images?
Generally in almost every case ist worth to augment your images since it improves the quality of the generated neuronal network by a good amount and also helps in some others aspects aswell.
One of the main reasons for augmentation is that you can create images in a very effective and fast manner. Collecting the required amount of data is one of the most crucial and time consuming part in most deep learning projects. A way to generate new data based off the already existing images is an excellent way to improve the product you are developing. The generated images differ from the already existing images but aren’t too different that you cant use them.
As mentioned above you can increase the quality of your neuronal network by augmenting your images. The images you create will also be trained by the neuronal network which makes it more flexible and ensures it can handle images which differ from the original images better since it learned more different variations of an image.
Image augmentation saves a lot of time since the process can be automated and there are a lot of diffrent ways to create new images. The benefits it brings to your project will most likely outweigh the time it takes to create the images.
What are augmentation pipelines and how can you use them effctively?
If you want to use the full potential of image augmentation it makes sense to delve into augmentation pipelines. With the help of augmentation pipelines you can augment your images in a more effective way to further increase the quality of your data.
An augmentation pipeline means a sequence of diffrent augmentations which are applied step by step. For exaple you can first rotate an image and apply a filter afterwards. With pipelines its possible to gradually modify your images until you reach the desired outcome.
Another very good reason for pipelines is that you can define as many pipelines as you wish. This means you can define diffrent pipelines for diffrent images. As an example if your original images are created from diffrent distances you can create an augmentation pipeline which zooms the images and assign this pipeline to the images which are created from greater distance.
One more crucial benefit augmentation pipelines provide is that by gradually modifying your images you are creating a lot of new data. Lets say you have a pipeline with three augmentations in it. The first one rotates the image. The second augmentation applies a greyscale filter and the last one zooms in. In this case you will generate 3 additional images from each original images that passes through the pipeline.
The first generate image will just be rotated. The second image will have the rotation and the greyscale filter applied to it. The last image will have all three augmentations combined so the image will be rotated, filtered and zoomed in. This way your neuronal network can train four diffrent versions of an image which makes it more flexible in the end.
Augmentation of image data is worth the effort in almost any situation. You can increase the quality of your image data and with better image data you directly increase the quality of your neuronal network since it uses better data to train and learn.
The time it takes to define proper pipelines is definitively worth if you compare it to the benefits you gain from adding augmentation pipelines to your project.
With the Annotation Tool we developed it is easy to defineand apply augmentation pipelines.
You can find more information about our Annotation.ComVISTEC Tool here.