Hello! My name is Theo, and I'm in charge of establishing the vision deep learning system in Arbeon's AI Team. Today, I'll be talking about "data augmentation." Large-scale data learning is prone to be slowed down due to a bottleneck and other issues. To resolve these issues, I'd like to share some methods I've tried! And let us jump right in! Recently, as our team proceeded with large-scale data learning, many issues emerged.

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I wanted an easy way to get a state-of-the-art image augmentation pipeline with no manual iteration, no separate models to train and no thinking. To provide that, I created MuarAugment (Model Uncertainty- And Randomness-based Augmentation), a GPU-supported Python package built on Pytorch, Albumentations and Kornia. There are a few resources you can use to master MuarAugment. There are Colab tutorials demonstrating MuarAugment. Most of the material in this article comes from those.

OpenCV is today's most popular image processing library, covering everything from classic image processing algorithms to cutting-edge deep learning pretrained models. However because OpenCV is not differentiable it mainly focuses on pre-processing tasks and cannot be embedded in an entire training process. Inspired by OpenCV, Kornia is based on PyTorch and designed to solve generic computer vision problems. It contains a set of routines for performing color space conversions, and differentiable modules for performing tasks such as image filtering and edge detection. Kornia's core code can efficiently define and compute the gradient of complex functions with reverse-mode auto-differentiation. Kornia consists of subset packages containing operators which can be inserted into neural networks to enable models to perform tasks such as image transformations, epipolar geometry, and depth estimation.