This article gives insights into the working mechanism of a Generative Adversarial Network and one of its popular variants, the Cycle Consistent Adversarial Network.
In this post, we’re going to be demonstrating how to to build a state of the art Bacterial Classification model on Gradient using the Fast.ai machine learning library.
In this post, we’ll build a machine learning pipeline to classify whether a patient has Pneumonia or not from chest x-ray images and then draw a heat-map on areas that the model used to make these decisions
Learn how to get access to models that have not yet been added to the Torchvision framework.
Learn to train a generative image model using Gradient° and then porting the model to ml5.js, so you can interact with it in the browser.
How to adapt major image augmentation techniques for object detection purposes. We also cover the implementation of horizontal flip augmentation.
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.
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.
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.
