In this R Tutorial, We’ll learn how to perform a very popular Computer Vision task which is Object Detection in R with YOLO (pre-trained Models). For this we’re going to use the
image.darknet package from https://github.com/bnosac. The good thing about this package is that it doesn’t require neither reticulate nor Python. It’s ported from the native
C code and hence the performance is good.
#devtools::install_github("bnosac/image", subdir = "image.darknet", build_vignettes = TRUE) library(image.darknet) #If required, Set new working directory where the final predictions imaged with bounding box will be saved #setwd(paste0(getwd(),"/projects/")) #Define Model - here it is Tiny Yolo yolo_tiny_voc <- image_darknet_model(type = 'detect', model = "tiny-yolo-voc.cfg", weights = system.file(package="image.darknet", "models", "tiny-yolo-voc.weights"), labels = system.file(package="image.darknet", "include", "darknet", "data", "voc.names")) #Image Detection x <- image_darknet_detect(file = "tinyyolo_in_R/google-car.png", object = yolo_tiny_voc, threshold = 0.19)