Data Augmentation for Custom Object Detection
Data Augmentation Steps for Custom Object Detection
So, If you are here then you might be enthusiast towards learning data augmentation, Object detection, machine learning, deep learning or image processing. And, you might have worked on image classification task where you might have done the data augmentation steps.
But, In Case of object detection, We have to draw bounding boxes for all the images. And, If we will apply the data augmentation steps then the number of images will increase and then again we need to do the labeling for those images. These is a method I will cover in this article how you can automate the labeling steps for augmented images.
What is “Data Augmentation” ?
Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks.
We now have idea what is data augmentation.
If you have worked on data augmentation in Image Classification problems you might aware of some data augmentation steps like:
