Synthesize photos from PhotoDNA using machine learning 🌱

Last update: Nov 23, 2022

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Deep Learning ribosome

Overview

Ribosome

Synthesize photos from PhotoDNA.

See the blog post for more information.

Installation

Dependencies

You can install Python dependencies using pip install -r requirements.txt. If you want to install the packages manually, here is a list:

PyTorch (torch, torchvision)
NumPy (numpy)
Pillow (Pillow)
tqdm (tqdm)

Pre-trained models

Ribosome is released with 4 pre-trained models:

coco-model.pt: trained on COCO 2017 train images
celeba-model.pt: trained on CelebA aligned+cropped images
nsfw-model.pt: trained on 100K SFW+NSFW images scraped from Reddit
coco+celeba+nsfw-model.pt: trained on the combination of the above

Use the models trained on NSFW data at your own risk.

Usage

Inference

Use the infer.py script to produce images from hashes:

python infer.py [--model MODEL] [--output OUTPUT] hash

The hash is a base64-encoded string, e.g. cVwhQ58OSCEOIwF+AigAkT0GAWdwAQs8o04KGYMfHBUANRUOAycUEFABCh6PABIghDBzCa4RTysQYVcvMDdkMypBPSyNAgRCcTf2AC9PfiYSWDw3KTcxPxM2HSqTDSIsgxJFFA+iihERcU4fHEY4Lj0xhw3QJN4OXQwbIzJjVTsUodIVVy3/FY8I/wcui11O.

Training

Datasets

Datasets consist of images paired with hashes, in the format of a CSV file with paths/hashes, and image files in a directory. The CSV file has two colums, path and hash (no header row). The hash is base64-encoded. Images are 100x100 in size. After producing such a CSV, it may be convenient to shuffle it and split it into a training set and validation set.

Example dataset

Ribosome includes an example dataset in this format, produced from COCO:

coco100x100.tar.gz: image files
coco-train.csv: training set hashes
coco-val.csv: validation set hashes

Preparing a dataset

To produce 100x100 images from an existing dataset, it may be convenient to use ImageMagick.

To resize image.jpg to 100x100 ignoring the original aspect ratio:

mogrify -resize '100x100!' image.jpg

To resize image.jpg to 100x100 by taking a center crop:

mogrify -resize '100x100^' -gravity Center -extent '100x100' image.jpg

You can process files in parallel using find / xargs, e.g. to convert all .jpg images using 24 threads:

find . -name '*.jpg' | xargs -n 1 -P 24 mogrify -resize '100x100!'

Ribosome does not provide code to compute PhotoDNA hashes, but such code is available in pyPhotoDNA.

Train a model

Use the train.py script to train a model on a dataset:

python train.py --train-data TRAIN_DATA ...

--train-data is the path to the train data CSV
Paths in the CSV are interpreted relative to --data-dir (or . if not supplied)
--val-data is the path to the validation data CSV; if provided, the script will report the validation loss after every epoch

See python train.py --help for all the options.

License

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Synthesize photos from PhotoDNA using machine learning 🌱

Related tags

Overview

Ribosome

Installation

Dependencies

Pre-trained models

Usage

Inference

Training

Datasets

Example dataset

Preparing a dataset

Train a model

License

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Releases(v1.0.0)

v1.0.0(Dec 20, 2021)

Owner

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