janni_tutorial

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janni_tutorial [2019/09/16 14:16]
shaikh [Training a model for your data]
janni_tutorial [2020/09/24 14:46]
twagner [Training a model for your data]
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 In case you want to use the general model ([[janni#​Download|Download here]]), you can skip this part and directly [[janni_tutorial#​denoise|denoise]] your images. ​ In case you want to use the general model ([[janni#​Download|Download here]]), you can skip this part and directly [[janni_tutorial#​denoise|denoise]] your images. ​
  
-In case you would like to train a model for your data, you need to copy a few movie files into a separate directory. We typically use at least 30 movies (unaligned) to train the model. Fewer might also work, but more often work much better. ​ The first thing you have to do is to create a configuration file for JANNI.+In case you would like to train a model for your data, you need to copy a few movie files into a separate directory. We typically use at least 30 movies (unaligned) to train the model. Fewer might also work, but more often work much better. ​Its recommended to copy movies from different data collections into different folders. If you later water do denoise binned micrographs,​ you should setup binning correctly. 
 + 
 +<note important>​ 
 +** Setup binning ** 
 +In case your movies are superresolution and you later want to denoise binned micrographs,​ the movies need to be binned during training. Therefore create a file ''​bin.txt''​ in the training folder with the movies that needs to be binned. Write the binning factor into this file (typically 2 or 4). 
 +</​note>​  
 + 
 +The next thing you have to do is to create a configuration file for JANNI.
  
 === Configuration === === Configuration ===
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 In case you need a description of all available parameters, type: In case you need a description of all available parameters, type:
 <​code>​ <​code>​
-janni_denoise.py ​predict ​-h+janni_denoise.py ​denoise ​-h
 </​code>​ </​code>​
  
 The following command will run the denoise the images in ''/​my/​averages/''​ and save the denoised images in ''/​my/​outputdir/​denoised/''​. The denoising here will run on GPU 0: The following command will run the denoise the images in ''/​my/​averages/''​ and save the denoised images in ''/​my/​outputdir/​denoised/''​. The denoising here will run on GPU 0:
 <​code>​ <​code>​
-janni_denoise.py ​predict ​/​my/​averages/​ /​my/​outputdir/​denoised/​ janni_imodel.h5 -g 0+janni_denoise.py ​denoise ​/​my/​averages/​ /​my/​outputdir/​denoised/​ janni_imodel.h5 -g 0
 </​code>​ </​code>​
 </​hidden>​ </​hidden>​
  • janni_tutorial.txt
  • Last modified: 2020/09/24 14:46
  • by twagner