Understanding darknet's yolo.cfg config files

Question

I have searched around the internet but found very little information around this, I don\'t understand what each variable/value represents in yolo\'s .cfg files. So

Answer 1

Although this is a quite old request of help, for the future users looking for an answer, you can find all the explanation on the Wiki page inside the most famous fork of the original Yolo project https://github.com/AlexeyAB/darknet/wiki

In particular, copying and pasting only the [net] part from here as follows:

[net]

• batch=1 - number of samples (images, letters, ...) which will be precossed in one batch
• subdivisions=1 - number of mini_batches in one batch, size mini_batch = batch/subdivisions, so GPU processes mini_batch samples at once, and the weights will be updated for batch samples (1 iteration processes batch images)
• width=416 - network size (width), so every image will be resized to the network size during Training and Detection
• height=416 - network size (height), so every image will be resized to the network size during Training and Detection
• channels=3 - network size (channels), so every image will be converted to this number of channels during Training and Detection
• inputs=256 - network size (inputs) is used for non-image data: letters, prices, any custom data

Anyway, you should even try to look in the relative Github/issues part for something, even naive, you want to know, because usually it has already been asked and answered.

Good luck.