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Trained with custom dataset model results

Open Muratoter opened this issue 3 years ago • 0 comments

I prepared custom dataset that 7000 images has only floor class and added to ade20k dataset. Now i have dataset with 28000 images. I changed floor annotations color with #040404 in my dataset, because floor class had that color in ADE20K dataset. So my floor annotation looks like this; 0101 jpg___fuse (#040404 color is very close to black. If you look carefully you will see the floor annotation)

and original images; 0101

It seems everything normal my dataset. Then i changed start_epoch and num_epoch values in config yaml file, num_epoch: 23 start_epoch: 20 epoch_iters: 5000 . Training process done with successfully and i have encoder and decoder model that names are encoder_epoch_23.pth, decoder_epoch_23.pth. Everything is seems normal here as well

I got results using theses models but result was not as expected. I got this result when i download model from here decoder_epoch_20.pth f89bce9bcb394a8a8aa785dfb847bf4a

And i got this result when using i trained model;

03046ef6e5ac431e8e33002854eebb05

Results seem to be getting worse. What could i be doing wrong?

Muratoter avatar Aug 11 '22 08:08 Muratoter