First tests with YOLOv3

YOLO (You only look once) provides a framework for realtime object detection which works for images and videos. Because of the method how the image is detected it’s called YOLO.

YOLO is written based on Darknet (a open source neural network library).

More information about the method and how YOLO works:

First test with YOLOv3

Download and prepare project darknet YOLO v3:

git clone
cd darknet

Download the weights a pre-trained model, which will be used to predict the objects.


Default threshold for confidence is set to 0.25 but can be changed with parameter -thresh.
Test with a first custom picture:

./darknet detect cfg/yolov3.cfg yolov3.weights '/home/user/Desktop/cats.jpg'
Console Output
Prediction of input picture

Test with other weights

./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights '/home/user/Desktop/cats.jpg'