Google Colab is, in very simple terms, a cloud-based coding environment. The user does not have to worry about setting up any infrastructure, and the code is executed on the Google’s servers. Google Colab might seem intimidating at first, but it’s really quite easy to use.

darknet_for_colab: darknet folder which was modified specifically to adapt with Colab environment (no MAKEFILE change necessary). yolov4_config.py: Taking the advantage of the direct python editing feature on Colab, you can now define training parameters just by double click on yolov4_config.py and edit it (Figure 1).

WxPython is a valuable library for creating GUIs in Python, but installing it on Google Colab can be challenging due to limitations in the platform’s environment. However, by following the steps outlined in this blog post, you can install WxPython on Google Colab and use it to create GUIs for your data science and machine learning projects.

Google Colab is incredibly easy to use on pretty much every level, especially if you’re at all familiar with Jupyter Notebooks. However, grabbing some large files and getting a couple of specific directories to work did trip me up for a minute or two.