Yolo Example

Finally found a way to identify objects in a picture. The neural network to do this is called Yolo. Here is a blog post about how to use Yolo in R.

Object detection in just 3 lines of R code using Tiny YOLO

I used the devtools install that is given in this blog post and it worked.

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

library(image.darknet)

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"))
x <- image_darknet_detect(file = "/home/esuess/classes/2018-2019/02 - Spring 2019/Stat654/Final/google_car.png", 
                          object = yolo_tiny_voc,
                          threshold = 0.19)

layer     filters    size              input                output
    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16
    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32
    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64
    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128
    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256
    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512
   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   13 conv   1024  3 x 3 / 1    13 x  13 x1024   ->    13 x  13 x1024
   14 conv    125  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 125
   15 detection
Loading weights from /home/esuess/R/x86_64-pc-linux-gnu-library/3.4/image.darknet/models/tiny-yolo-voc.weights...Done!

/home/esuess/classes/2018-2019/02 - Spring 2019/Stat654/Final/google_car.png: Predicted in 2.440059 seconds.
Boxes: 845 of which 4 above the threshold.
person: 24%
car: 96%
person: 40%
bicycle: 50%

x <- image_darknet_detect(file = "/home/esuess/classes/2018-2019/02 - Spring 2019/Stat654/Final/busax.jpg", 
                          object = yolo_tiny_voc,
                          threshold = 0.25)

layer     filters    size              input                output
    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16
    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32
    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64
    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128
    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256
    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512
   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024
   13 conv   1024  3 x 3 / 1    13 x  13 x1024   ->    13 x  13 x1024
   14 conv    125  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 125
   15 detection
Loading weights from /home/esuess/R/x86_64-pc-linux-gnu-library/3.4/image.darknet/models/tiny-yolo-voc.weights...Done!

/home/esuess/classes/2018-2019/02 - Spring 2019/Stat654/Final/busax.jpg: Predicted in 2.479083 seconds.
Boxes: 845 of which 4 above the threshold.
train: 73%
bus: 28%
person: 86%
person: 55%