This post is a quick walkthrough of the different data augmentation methods available in Detectron2 and their utility for augmenting overhead imagery. I’ll also go over a quick way to implement them.

This post contains details of how I set up my shell and environment. I use Windows, Mac, and Linux on a daily basis, so I have different setups for different purposes, but I try to make them similar when I can.

This post contains details of how I set up my shell and environment. I use Windows, Mac, and Linux on a daily basis, so I have different setups for different purposes, but I try to make them similar when I can. You can see the software I use and how I customize it in the linked posts; this post will focus on setup.

This post contains details of how I set up my shell and environment. I use Windows, Mac, and Linux on a daily basis, so I have different setups for different purposes, but I try to make them similar when I can. You can see the softwhere I use and how I customize it in the linked posts; this post will focus on setup.

I ran into an interesting issue when debugging in Python that I thought was worth sharing. It came up when I was visualizing some results from an object detector. I had a class for an object detector and one of the things it would check was that each prediction was associated with a valid object in the object dictionary. Here is a mock-up of the relevant parts (this is not the real class, just a toy example so as not to distract from the point).

This posts contains some of my notes from switching to PyTorch after having worked with TensorFlow and Keras for a long time.

There are a lot of great resources on the web that contain implementations of deep learning architectures, but they can be a little hard to find. This post aims to highlight and categorize some of the best resources that I have found. Most repositories have either Tensorflow or PyTorch implementations, so this post is divided by frameworks.