Generative Models How To Use CycleGAN for Age and Race Conversion If you've been on any sort of social media platform this year, you've probably seen people uploading a picture of themselves right next to another picture of what they'll look
Deep Learning Going beyond torchvision models ResNets, DenseNets and Inception networks are undoubtedly some of the most powerful models out there for performing image classification and object recognition. These models have shown some promising results  in
Series: Data Augmentation Data Augmentation for Bounding Boxes: Rethinking image transforms for object detection How to adapt major image augmentation techniques for object detection purposes. We also cover the implementation of horizontal flip augmentation.
Series: Data Augmentation Data Augmentation for Bounding Boxes: Scaling and Translation We implement Scale and Translate augmentation techniques, and what to do if a portion of your bounding box is outside the image after the augmentation.
Computer Vision Data Augmentation for Bounding Boxes: Rotation and Shearing This is part 3 of the series where we are looking at ways to adapt image augmentation techniques to object detection tasks. In this part, we will cover how to implement how to rotate and shear images as well as bounding boxes using OpenCV's affine transformation features.
Series: Data Augmentation Data Augmentation For Bounding Boxes: Building Input Pipelines for your detector Previously, we have covered a variety of image augmentation techniques such as Flipping, rotation, shearing, scaling and translating. This part is about how to bring it all together and bake it into the input pipeline for your deep network.
