This commit is contained in:
deep pomf 2018-02-26 10:09:54 -05:00
commit 87e37da0d7
2 changed files with 8 additions and 2 deletions

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@ -70,6 +70,10 @@ Decensored images will be saved to the "decensor_output_images" directory. Paste
## II. Train the pretrained model on custom dataset ## II. Train the pretrained model on custom dataset
You must have a GPU for training since training on a CPU will take weeks.
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.
Put your custom dataset for training in the "data/images" directory and convert images to npy format. Put your custom dataset for training in the "data/images" directory and convert images to npy format.
``` ```
@ -77,12 +81,14 @@ $ cd training_data
$ python to_npy.py $ python to_npy.py
``` ```
Train pretrained model on your custom dataset. To train, run
``` ```
$ python train.py $ python train.py
``` ```
If desired, you can train the pretrained model on your custom dataset.
Training can be done separately for mosaics with train_mosaic.py, but decensor.py is not yet compatible with mosaic decensorship models. Training can be done separately for mosaics with train_mosaic.py, but decensor.py is not yet compatible with mosaic decensorship models.
# To do # To do

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@ -137,7 +137,7 @@ def get_points():
#rotate random amount between 0 and 90 degrees #rotate random amount between 0 and 90 degrees
m = scipy.ndimage.rotate(m, np.random.random()*90, reshape = False) m = scipy.ndimage.rotate(m, np.random.random()*90, reshape = False)
#set all elements greater than 0 to 1 #set all elements greater than 0 to 1
m[m > 0] = 1 m[m > 0.5] = 1
mask.append(m) mask.append(m)