You could spin up a local instance of cvat, which is a FOSS labelling tool that has a ton of features, and put whatever yoi want through the pipeline.

https://github.com/LAION-AI/CLIP-based-NSFW-Detector uses CLIP, all released under MIT

Depending on the purpose I can assist

Likely no commercial ones since payment processes make it difficult to process payments for porn. There are a ton you could self host though.

I can second this, cvat is pretty easy to install and use as well

Yeah, I would also be willing to watch po... ehm, label data for you.
In the name of science.

Seriously though I wonder how the NSFW filters on SD etc work... I wonder how much hand labeled adult material was gathered before it could mostly be automated.

I'm kind of amused by the idea of researchers sitting in their office, all sciency, browsing pornhub and taking notes.

I kind of like to hope the researchers never had to look at the dataset much ..

Ps: i am kind of a data scientist :p

Mechanical Turk actually allows this. There's a special "Adult Content" Qualification.

NSFW means anything from Victoria's Secret ads to Documenting Reality message boards.

Remote contractors on fiverr/upwork etc. can usually do regular porno but if you're working with CP or something you're going to need to hire on site staff.

Before you go through this work, you know that there are existing datasets, right?

See: https://github.com/EBazarov/nsfw_data_source_urls and https://github.com/alex000kim/nsfw_data_scraper for example.

If you want to train a NSFW classifier, use the existing sets first. And use a pre-trained Imagenet classifier first and fine tune it. This will get you 90+% of the way there. It would make sense for you to have your own testing set to make sure that it works for your use-case (CVAT or VoTT work fine), but goodness, don't start from scratch.

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