Drones not detected after training of Tiny YOLOv3 about drone-net HOT 13 OPEN

I don't know, even when using labelimg for image labeling following YOLO format they look like this...
0 0.534304 0.380769 0.640333 0.584615
Feel free to close the issue since as for now, I think, my problem is solved using this labeling, even though I have not tried to convert your labels in this format.
Thanks for your feedback and thanks again for sharing your work 🙏

from drone-net.

@damnko I see that you always have count = 0. Perhaps check if all your training images are correctly labeled and configured (e.g. in same directory)?
Also, feel free to start training from weights/yolo-drone.weights instead of from darknet53.conv.74.

from drone-net.

Hi @chuanenlin thanks for your prompt reply.
Yes, the images and labels are both in the same folder and I've downloaded everything from your repo, I didn't do the labeling myself, so I suppose they're correct.

That count should indicate the number of times the object is detected in a specific region for that image? What should I expect to see regarding that number so I can check on the full log?
I will try to start training from your weights, but I suppose it should work anyway, starting from darknet53.conv.74 correct?

from drone-net.

@damnko Yes, therefore count = 0 should indicate either some kind of error (e.g. labeling) or that it needs more iterations of training. You should expect to see several lines of non-NAN values for IOU, Class, etc. and count > 0 after decent training iterations and correct configuration.

If you start from darknet53.conv.74, it would mean starting from "scratch" (take note that I trained for a couple of days on a NVIDIA Tesla GPU) while starting from weights/yolo-drone.weights would mean starting from where I left off.

from drone-net.

Oh, a couple of days... But were you training the standard, non-tiny, version right? But then what does 1930: 0.000000 indicate? Shouldn't that be the loss you also refer to in the blog post?

from drone-net.

@damnko That's correct. FYI, feel free to open an issue here and look if anyone has also faced a similar issue.

from drone-net.

Ok thanks, I will investigate a little bit more and then will open an issue on the darknet repo. Thanks for now, I will keep you posted and come back to close this issue.

from drone-net.

Hi @chuanenlin , sorry to ask you again. But I've tried to follow another tutorial with a different dataset and it worked, now I'm trying the same settings, with your images/labels and I'm having the same problem.

The labels of the other tutorial (which works) is in the following form:
0 0.469603 0.48314199999999996 0.797766 0.795552
While your label has this format:
0 167.5 116.5 175.0 157.0

So, the first seems to have relative positions/dimensions, and yours absolute. Is there something wrong, or both should work? I wonder if the problem is there.
Thanks again for your help

from drone-net.

@damnko May I ask which repository for YOLO are you using? Some variants such as AlexeyAB's version require different labeling formats, such as the one you mentioned. The labels in this repo should work with the original (pjreddie's) version.

from drone-net.

This is the version I compiled: https://github.com/pjreddie/darknet

from drone-net.

That's odd - I trained the weights with the labels in this repo so the formatting should be fine. 🤔

from drone-net.

@damnko What OS are you on? I am having a similar problem with normal YOLO. Also, what was the other tutorial?

from drone-net.

Hi @trigaten , I was using Linux Mint. It was some time ago and can't remember the other tutorial I was looking at but I guess it was this one: https://www.learnopencv.com/training-yolov3-deep-learning-based-custom-object-detector/

from drone-net.