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janni_tutorial [2019/07/10 10:05] twagner [Training a model for your data] |
janni_tutorial [2019/07/16 10:01] twagner [Training a model for your data] |
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==== Training a model for your data ==== | ==== Training a model for your data ==== | ||
- | In case you want to use the general model ([[janni# | + | In case you want to use the general model ([[janni# |
+ | |||
+ | In case you would like to train a model for your data, the first thing you have to do is to create a configuration file for JANNI. In the following I assume you named it // | ||
<code json config.json> | <code json config.json> | ||
{ | { | ||
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* **saved_weights_name**: | * **saved_weights_name**: | ||
In principle you only have to adapt the paths. The other could keep as they are. | In principle you only have to adapt the paths. The other could keep as they are. | ||
- | We typically use at least 30 movies to train the model. Less might also work, more work often much better. | + | We typically use at least 30 movies |
To run the training on gpu 0: | To run the training on gpu 0: | ||
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==== Denoise ==== | ==== Denoise ==== | ||
- | After you trained | + | With a trained model (either trained by you or the general model) |
To denoise a set if images you have to tell JANNI three **mandatory** arguments: | To denoise a set if images you have to tell JANNI three **mandatory** arguments: | ||
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Here is now how you do the actual denoising: | Here is now how you do the actual denoising: | ||
- | It is assumed that you run the command in a directory | + | It is assumed that you run the command in a directory with your model file '' |
The following command will run the denoising on GPU 0: | The following command will run the denoising on GPU 0: |