I didn't get any detcted boxes after training new data about yolo-windows HOT 4 CLOSED

@anas-899 Yes, should be in .cfg:
output= (5 x 2 + C) x S x S
classes= C
side= S

1. In my fork in yolo.c, CLASSNUM should be not less than classes, everything else in source code is automatically set
2. use only jpeg and jpg format for training
3. train at least 1000 batches
4. for training use pretrained conv-model: http://pjreddie.com/media/files/extraction.conv.weights

If you get NAN when you training, or when you are trying to continue training from checkpoint: https://groups.google.com/forum/#!searchin/darknet/NAN|sort:relevance/darknet/-5DRax1SS2w/aDjRl15HEAAJ

• reduce the learning rate(e.g. 1/10 of the origin), this can help (learning_rate= in .cfg file)
• try to train on Linux (where the problem occurs much less frequently) by using https://github.com/pjreddie/darknet and then use already trained .weights for this fork yolo-windows

from yolo-windows.

@AlexeyAB
Thanks a lot for your fast replay.
Actually I tried everything but the training on windows is not giving any correct .weight file
I tried to train the data and same example of : http://guanghan.info/blog/en/my-works/train-yolo/
but actually the output weight file didn't detect anything

I think the only way to success is by training over Linux

from yolo-windows.

I solved the saving weight issue.
It needs to change in two positions:
first:
src/parser.c
you should change the line
FILE *fp = fopen(filename, "w");
to
FILE *fp = fopen(filename, "wb");

second:
src/utils.c
you should change the line
if(c==' '||c=='\t'||c=='\n') ++offset;
to
if(c==' '||c=='\t'||c=='\n'||c=='\r') ++offset;

then the training on windows will save the weight correctly.

from yolo-windows.

@anas-899 Thank you! I added this fix to rep.

from yolo-windows.