In this section, I put the code and the dataset of my rubik's cube to building a custom object detector to detect rubik's cube using YOLOv3 in Python. Training process is done using the Darknet framework and the real-time detector implemented with OpenCV DNN module.
In the next table, I briefly described the contents of this section.
| Name | Its Function |
| Dataset | Folder contains images and labels folders. |
| generate.py | Script to generate train.txt and test.txt files. |
| custom | Folder contains needed files (train.txt, test.txt, objects.names, yolov3-tiny.cfg, and trainer.data) for training. This folder must be pasted on the main directory of darknet |
| yolo_opencv.py | Real-time rubik's cube detector, it reads a stream of frames from the webcam the then detects the rubik's cube in each one. |
| model.data | Pre-trained model to detect rubik's cube, can be downloaded from here. |
http://emaraic.com/blog/yolov3-custom-object-detector
https://www.youtube.com/watch?v=tlVfsgRokcQ
