Computer Vision Object Detection in R with YOLO Pre-trained Models

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 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.

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#devtools::install_github("bnosac/image", subdir = "image.darknet", build_vignettes = TRUE)


#If required, Set new working directory where the final predictions imaged with bounding box will be saved


#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)

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