Abstract
Data augmentation is a technique to increase the diversity of dataset without an effort to collect any more real data but still help improve your model accuracy and prevent the model from overfitting. In this post, you will learn to implement the most popular and efficient data augmentation procedures for object detection task using Python and OpenCV. The set of data augmentation methods that are about to be introduced includes: (1) Random Crop, (2) Cutout, (3) ColorJitter, (4) Adding Noise, and (5) Filtering.
The code of this work is publicly available at https://github.com/tranleanh/data-augmentation.
Email: tranleanh.nt@gmail.com
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