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dbpprt / u-2-net-portrait

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
Apache License 2.0
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Loss does not change from nan #12

Closed gyupro closed 2 years ago

gyupro commented 2 years ago

Hi, I am really appreciate of your work

I am enjoying with your trained model. I am trying to train my own dataset, I got stuck with that loss keeps showing me nan

log : image

src image is source image of mask. Cannot upload here due to copyright

mask: image

env: env.txt

xuebinqin commented 2 years ago

Thanks for you interests. You could try to overfit one or two pairs of your training set, if it still doesn't work, you probably have to imsave the ground truth and image loaded by the dataloader to see whether they are correctly loaded and normalized.

Besides, you could also double check the type and range of your images and labels as well as the sigmoid functions to see if they are correct.

On Wed, Jan 19, 2022 at 1:44 PM gyupro @.***> wrote:

Hi, I am really appreciate of your work

I am enjoying with your trained model. I am trying to train my own dataset, I got stuck with loss keeps showing me nan

log : [image: image] https://user-images.githubusercontent.com/79894531/150104609-8e125c03-7cc0-4803-aaf1-f33add61ac20.png

image is mask of source image. Cannot upload here due to copyright

mask: [image: image] https://user-images.githubusercontent.com/79894531/150104938-a56d05e6-2b28-4f6a-b07f-511e78f04aa5.png

env: env.txt https://github.com/dennisbappert/u-2-net-portrait/files/7895894/env.txt

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-- Xuebin Qin PhD Department of Computing Science University of Alberta, Edmonton, AB, Canada Homepage: https://xuebinqin.github.io/

gyupro commented 2 years ago

Thanks for your prompt reply.

I checked all details you provided, those were okay but I found the problem. I fixed it by chaing parameter "detailed_masks = True" in dataset.yaml configuration file.

I think I have to deliberately look into it what has been set wrong.

Thank you.