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auto2d_tutorial [2019/05/28 09:46] twagner [Classify] |
auto2d_tutorial [2019/08/15 15:35] twagner [Classify] |
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- | ====== How to use SPHIRE' | + | ====== How to use SPHIRE' |
==== Download & Install ==== | ==== Download & Install ==== | ||
You can find the download and installation instructions here: [[auto_2d_class_selection|Download and Installation]] | You can find the download and installation instructions here: [[auto_2d_class_selection|Download and Installation]] | ||
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" | " | ||
" | " | ||
- | " | + | " |
+ | " | ||
+ | " | ||
} | } | ||
} | } | ||
</ | </ | ||
- | Copy this into a new file called '' | + | The fields have the following meaning: |
+ | * **input_size**: | ||
+ | * **batch_size**: | ||
+ | * **good_classes**: | ||
+ | * **bad_classes**: | ||
+ | * **pretrained_weights**: | ||
+ | * **saved_weights_name**: | ||
+ | * **learning_rate**: | ||
+ | * **nb_epoch**: | ||
+ | * **nb_early_stop**: | ||
+ | * **train_valid_thresh**: | ||
+ | * **max_valid_img_per_file**: | ||
- | To run the classification I suppose you want to seperate | + | Copy this into a new file called '' |
+ | |||
+ | To run the classification I suppose you want to separate | ||
This is the command to run: | This is the command to run: | ||
< | < | ||
- | sp_auto2d_predict.py -i path/ | + | sp_cinderella_predict.py -i path/ |
</ | </ | ||
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==== Training ==== | ==== Training ==== | ||
- | If you would like to train Auto2D | + | If you would like to train Cinderella |
- | First you have to separate your good and bad classes into separate files. Create two folders, on containing good classes (e.g '' | + | First you have to separate your good and bad classes into separate files. Create two folders, on containing good classes (e.g '' |
Then specify the paths into a config file like this: | Then specify the paths into a config file like this: | ||
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" | " | ||
" | " | ||
- | " | + | " |
} | } | ||
} | } | ||
</ | </ | ||
+ | The fields have the following meaning: | ||
+ | * **input_size**: | ||
+ | * **batch_size**: | ||
+ | * **good_classes**: | ||
+ | * **bad_classes**: | ||
+ | * **pretrained_weights**: | ||
+ | * **saved_weights_name**: | ||
+ | * **learning_rate**: | ||
+ | * **nb_epoch**: | ||
+ | * **nb_early_stop**: | ||
The next step is to run the training: | The next step is to run the training: | ||
< | < | ||
- | sp_auto2d_train.py -c example_config.json --gpu 1 | + | sp_cinderella_train.py -c example_config.json --gpu 1 |
</ | </ | ||
This will train a classification network on the GPU with ID=1. After the training finishes, you get a '' | This will train a classification network on the GPU with ID=1. After the training finishes, you get a '' | ||