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Merge branch 'master' of https://github.com/deeppomf/DeepMindBreak
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87e37da0d7
@ -70,6 +70,10 @@ Decensored images will be saved to the "decensor_output_images" directory. Paste
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## II. Train the pretrained model on custom dataset
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## II. Train the pretrained model on custom dataset
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You must have a GPU for training since training on a CPU will take weeks.
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Your custom dataset should be 128 x 128 images of uncensored vaginas and penises cropped from hentai. The more images, the better: I used 70,000 images for training. Censoring these images yourself is unnecessary.
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Put your custom dataset for training in the "data/images" directory and convert images to npy format.
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Put your custom dataset for training in the "data/images" directory and convert images to npy format.
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```
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```
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@ -77,12 +81,14 @@ $ cd training_data
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$ python to_npy.py
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$ python to_npy.py
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```
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```
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Train pretrained model on your custom dataset.
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To train, run
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```
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```
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$ python train.py
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$ python train.py
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```
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```
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If desired, you can train the pretrained model on your custom dataset.
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Training can be done separately for mosaics with train_mosaic.py, but decensor.py is not yet compatible with mosaic decensorship models.
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Training can be done separately for mosaics with train_mosaic.py, but decensor.py is not yet compatible with mosaic decensorship models.
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# To do
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# To do
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2
train.py
2
train.py
@ -137,7 +137,7 @@ def get_points():
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#rotate random amount between 0 and 90 degrees
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#rotate random amount between 0 and 90 degrees
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m = scipy.ndimage.rotate(m, np.random.random()*90, reshape = False)
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m = scipy.ndimage.rotate(m, np.random.random()*90, reshape = False)
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#set all elements greater than 0 to 1
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#set all elements greater than 0 to 1
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m[m > 0] = 1
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m[m > 0.5] = 1
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mask.append(m)
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mask.append(m)
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