Simple script to create your own .cfg to train a Darknet YOLO model
Script to create your own .cfg to train a Darknet YOLO model
First clone the repo or download it by clicking here: click
In order to create the .cfg file for your custom YOLOv4 model run:
$ python cfg_create.py -i yolov4.cfg -c <number of classes to train>
this will create a file named yolov4_custom.cfg in the same path as yolov4.cfg.
Example 2:
If you want to train a tiny-yolov3 to detect 6 different classes you can run:
$ python cfg_create.py -i yolov3-tiny.cfg -c 6
| argument name | default | Description |
|---|---|---|
"-input" or "-i" |
"path to the .cfg" |
|
"-classes" or "-c" |
"How many classes to detect" |
|
"-num_images" or "-n" |
6000 | "OPTIONAL: Number of training images " |
"-width" or "-wi" |
416 | "OPTIONAL: network size- width " |
"-height" or "-he" |
416 | "OPTIONAL: network size- height " |
"-batches" or "-b" |
64 | "OPTIONAL: batch size during train " |
"-subdivisions" or "-sub" |
32 | "OPTIONAL: subdivisions during train " |
"-no_flip" |
True | "OPTIONAL: use -no_flip to set flip=0 " |
only use -no_flip if you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, …).
If you are in doubt about any parameter value you should check AlexeyAB darknet repo