Closed icklerly1 closed 1 year ago
please do not change anchors in yaml file because this is an anchor-free detector(which is equivalent to that the size of the anchor box is the reciprocal of the scaling factor of the feature map) and changing to anchor-based models is not supported. you can change your learning rate if necessary.
Alright. So I want to adapt the learning rate:
The calculation for the learning rate is lr_per_img: 0.00015625 # total_lr = lr_per_img batch_size_per_gpu len(devices)
What is the desired total_lr ? Otherwise I cannot calculate it for my custom training.
Generally 0.01 or less
there is no fixed way to adjust learning rate, it depends on yourself
Hi,
thank you. I now tried to adjusted the learning rate to a lower value.
I additionally checked our annotations as well. Below you can see a picture:
We only have 2 classes: "head" and "person".
In addition I changed the number of classes in params/model/edgeyolo.yaml:
from nc: 80 to nc: 2
I can start my training, the loss is decreasing a bit but right now I only get weird looking results, like this:
Do you have any idea what might be going wrong? I am really out of ideas..
Thanks a lot for your help! I found an error in my dataset preparation.
It now works great! Thanks again.
Hi, I am training edgeyolo on a custom dataset. Unfortunately the model does not detect any objects. Also the training loss does not decrease as expected.
number of images: train images: 15000 val images: 4370
and the auto calculated anchors in yolov4 would be: [5, 10], [13, 30], [25, 64], [38,123], [51,230], [87,158], [82,340], [136,420], [256,492]
My questions now:
Do I need to edit the anchors in params/model/edgeyolo.yaml ? And do I need to change the learning rate maybe?