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DATA INPUT and MODEL LOADING #203
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Thank you for the reply!! It was really helpful. I had a few more doubts, so I generated the synthetic data using my optical setup details. But the images have number of features = 377. I didn't get the logic of taking I am getting such a high number of features because my setup resolution is 0.106µm/px. Hence, the pixel size is 5.86µm with 55x magnification, and the (MAX_Z - MIN_Z) is just 40µm. So if put Z_scale = 0.106e-6, No. of features become 377, and if I take Z_scale = 5.86e-6, No. of features become 6, which is very low. Do you guys have any suggestions on this? @BenjaminMidtvedt |
Hi, Can you please tell how one can get the 'ProcessedField', 'Traces' and 'mapping' for their video data, I am not able to find any lead from the papers about any numerical algorithms for pre-processing of data before providing it to the model. In a 3D holography model, it seems necessary to have those for the model to work. Any lead would be of great help. @BenjaminMidtvedt @JesusPinedaC |
HI,
I am trying to replicate the paper example of 'inline_holography_3d_tracking'. The code is working fine but I am facing few issues. I would be very thankful if you guys can help.
I am trying to save the trained model using
save.model(my_model)
, but when I am loading the weights usingload.weights('my_model')
, I am facing this error. "ValueError: Unable to restore custom object of class "MeanMetricWrapper" (type _tf_keras_metric)." Is there any way to save and load the model. I have tried h5 file , and it gives the same issue.I am having a measurement video of holopgrahic particles on which I want to use this U_net model. How should I input my video. Does it need some kind of preprocessing. I am confused because in the example the input dataset is having the video in .mat format while my video is in .AVI format. Also, in the example there are traces and mapping. How should I get it for my dataset?
For the traces of the particles do I need to use the MAGIK model? is it possible to do with U_net?
Thank you for your time and this framework.
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