Fast.ai in R: How to Make a Computer Vision Model within an R Environment
Yes, R programmers can make machine learning models, too!
In this presentation, we will discuss using the latest techniques in computer vision as an important part of “AI for Good” efforts, namely, enhancing wildlife preservation. We will present how to make use of the latest technical advancements in an R setup even if they are originally implemented in Python.
A topic rightfully receiving growing attention among Machine Learning researchers and practitioners is how to make good use of the power obtained with the advancement of the tools. One of the avenues in these efforts is assisting wildlife conservation by employing computer vision in making observations of wildlife much more effective. We will discuss several such efforts during the talk.
One of the very promising frameworks for computer vision developed recently is the Fast.ai wrapper of PyTorch, a Python framework used for computer vision among other things. While it incorporates the latest theoretical developments in the field (such as one cycle policy training) it provides an easy to use framework allowing a much wider audience to benefit from the tools, such as AI for Good initiatives run by people who are not formally trained in Machine Learning.
During the presentation we will show how to make use of a model trained using the Python’s fast.ai library within an R workflow with the use of the reticulate package. We will focus on use cases concerning classifying species of African wildlife based on images from camera traps.
- Find the code and assets for this presentation here.
- Want to learn how to write high-quality, production-ready R code? See Marcin Dubel’s eRum/useR presentation on Production-Ready R Code here.
- Learn more about Appsilon’s AI For Good Initiative here.
- Learn more about Appsilon’s ML wildlife preservation project here.
Does your company or non-profit need help with a computer vision project? Reach out to us at [email protected].