This is a set of scripts that allows for an automatic collection of tens of thousands of images for the following (loosely defined) categories to be later used for training an image classifier:

• porn - pornography images
• hentai - hentai images, but also includes pornographic drawings
• sexy - sexually explicit images, but not pornography. Think nude photos, playboy, bikini, etc.
• neutral - safe for work neutral images of everyday things and people
• drawings - safe for work drawings (including anime)

Here is what each script (located under scripts directory) does:

• 1_get_urls_.sh - iterates through text files under scripts/source_urls downloading URLs of images for each of the 5 categories above. The ripme application performs all the heavy lifting. The source URLs are mostly links to various subreddits, but could be any website that Ripme supports. Note: I already ran this script for you, and its outputs are located in raw_data directory. No need to rerun unless you edit files under scripts/source_urls.
• 2_download_from_urls_.sh - downloads actual images for urls found in text files in raw_data directory.
• 3_optional_download_drawings_.sh - (optional) script that downloads SFW anime images from the Danbooru2018 database.
• 4_optional_download_neutral_.sh - (optional) script that downloads SFW neutral images from the Caltech256 dataset
• 5_create_train_.sh - creates data/train directory and copy all *.jpg and *.jpeg files into it from raw_data. Also removes corrupted images.
• 6_create_test_.sh - creates data/test directory and moves N=2000 random files for each class from data/train to data/test (change this number inside the script if you need a different train/test split). Alternatively, you can run it multiple times, each time it will move N images for each class from data/train to data/test.

• Docker

$ docker build . -t docker_nsfw_data_scraper
Sending build context to Docker daemon  426.3MB
Step 1/3 : FROM ubuntu:18.04
 ---> 775349758637
Step 2/3 : RUN apt update  && apt upgrade -y  && apt install wget rsync imagemagick default-jre -y
 ---> Using cache
 ---> b2129908e7e2
Step 3/3 : ENTRYPOINT ["/bin/bash"]
 ---> Using cache
 ---> d32c5ae5235b
Successfully built d32c5ae5235b
Successfully tagged docker_nsfw_data_scraper:latest
$ # Next command might run for several hours. It is recommended to leave it overnight
$ docker run -v $(pwd):/root/nsfw_data_scraper docker_nsfw_data_scraper scripts/runall.sh
Getting images for class: neutral
...
...
$ ls data
test  train
$ ls data/train/
drawings  hentai  neutral  porn  sexy
$ ls data/test/
drawings  hentai  neutral  porn  sexy

• Install fastai: conda install -c pytorch -c fastai fastai
• Run train_model.ipynb top to bottom

I was able to train a CNN classifier to 91% accuracy with the following confusion matrix:

As expected, drawings and hentai are confused with each other more frequently than with other classes.

Same with porn and sexy categories.