How to train 2D super-resolution imageStack at Decode?

[HypnosWei]

Hello,
I am new to the DECODE community, I am a student trying to use decode on my own 2D image data because the performance in the paper looks awesome, After looking up SMAP UserGuide, I summarize the 2D training workflow may have many steps to do.

first, load files and set cam parameters, after localizing, I use calibrate3DSplinePSF to get the simulation PSF, but I don't know how to set the parameter in 3D calibration, so I just choose bi dir, and the other parameter just use default. Also, I watch the video on youtube [https://www.youtube.com/watch?v=zoWsj3FCUJs], I set up all the parameters for the training process, and training in cmd, but the loss always be nan(see below)

I provide the training parameters files, could you see what is the problem? Thank you!
brain_train.txt

[JipWulffele]

Hi,
I am having the same problem.
I have a 3D bead calibration (but no astigmatism or anything so symetrical psf) which I processed with SMAP. Then when generating the simulated data I set the Z range to [0,0] and overall the simulated data looks good.
In the training file I set 'param.HyperParameter.disabled_attributes = 3' and 'Evaluation: match_dims: 2' as suggested in an earlier discussion but the training does not seem to work (L:nan all the time). I tried to fit some data with the trained model anyway (after 70 epochs) but it only detects stuff on the edges of the image.
Thanks a lot for your help!

[Haydnspass]

@JipWulffele I would recommend to not to set the z range to [0, 0], because if you do, the network will only be trained on data that is totally in the focal plane which will then not match the experimental data you are trying to fit.
The explanation for the disabled_attributes is, that these values are still simulated and predicted but the model is not punished for wrong z predictions.

Meanwhile we discuss how to make 2D training easier. Stay tuned :)

[Haydnspass]

Hello, I am new to the DECODE community, I am a student trying to use decode on my own 2D image data because the performance in the paper looks awesome, After looking up SMAP UserGuide, I summarize the 2D training workflow may have many steps to do.

first, load files and set cam parameters, after localizing, I use calibrate3DSplinePSF to get the simulation PSF, but I don't know how to set the parameter in 3D calibration, so I just choose bi dir, and the other parameter just use default. Also, I watch the video on youtube [https://www.youtube.com/watch?v=zoWsj3FCUJs], I set up all the parameters for the training process, and training in cmd, but the loss always be nan(see below)

I provide the training parameters files, could you see what is the problem? Thank you! brain_train.txt

@HypnosWei Could you send me the your calibration file (and if possible the beads) as well? Then I can take a look.
If you want you can use this upload link: https://oc.embl.de/index.php/s/yOuNknBkQblSGwH

[HypnosWei]

@Haydnspass I just uploaded the files, thank you!

[Haydnspass]

@HyposWei which version of SMAP are you using? Do you use the compiled version or the git repository?

[HypnosWei]

I use compiled version, the SMAP.exe,

[Haydnspass]

Dear @HypnosWei

Sorry for the late reply. I was checking your training on my machine and saw some suspicious things.
To really help you out, could I ask you to upload the following to the own cloud link (as before):

1. RAW .tiff of your beads
2. The dataset you want to fit (from which you extracted the beadstacks).

I will then double check and train a model.

Best,
Lucas