Data Augmentation in darknet and train YOLO

The same question is being asked by me on stackoverflow and I am pasting the same question here. I hope to get some useful links to solve the problem. Problem is stated below

I had been wondering whether data augmentation is done using config file of yolo or is implemented in src/image.c. I am talking with reference to darknet and study of issue #1408.

I am addressing this issue after a lot of research. If i look into the answer of issue #1408 darknet, it is clear that augmentation is implemeted in image.c located in src folder of darkenet repository but on the other side if I look into the yolo.cfg file and points to line 13, 14, 15 and 16 parameters of augments are defined there as angle, saturation, exposture and hue .

I am confused. If I look into the file image.c located in the src directory, methods for the augmentations are load_data_detection() and rotate_crop_image but since these are just methods. How values are supplied to them.

Another point where i am throwing light is about angle rotation support yolo. I read somewhere that yolo angle support is not provided. Can some elaboration can be made to this point.

During training yolo model with the images I got results in the terminal which shows that image number has increased during training like I was doing training with 400 images but during training, i got the below output

1180: 1.007143, 1.776118 avg loss, 0.001000 rate, 3.467960 seconds, 75520 images, 3.077078 hours left

Any useful helps or links can be worth to me.