Tools are changing quickly, and modern Deep Learning has really only existed for a blink of an eye in the grand scheme of things. Stay up to date by learning more below.
A step-by-step guide for getting started with GradientCI.
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.
The Community is designed with several top-level categories covering different use-cases as well as Paperspace specific categories. Check out the features below!
Our goal in the recent performance improvements and cloud reliability is to increase uptime, be more proactive, and respond more quickly to anything that comes up.
Gradient° has been updated in response to a ton of feedback from the community. Here's a roundup of some of the things we've added recently.
