I have tested 10 sets of data, but it doesn't feel very accurate. I want to know how you define the value of nsfwConfidence

[SiterZzz]

I have tested 10 sets of data, but it doesn't feel very accurate. I want to know how you define the value of nsfwConfidence

[SiterZzz]

NsfwConfidence has a value of 0.1 to 0.4

[absolute-heike]

Hey thanks for asking :)

We defined genitals, female breasts, etc. as NSFW. In other words explicit content.

Being naked while wearing a bra or covering your body parts on the other hand is considered Safe for Work.
Note that detecting violence, blood and other topics considered as NSFW is not part of NSFWDetector.

I am working on a way to share the dataset without getting into legal problems here.

If you need your own definition of NSFW, then I'd suggest to just train the model yourself. It's very easy and you don't need a huge amount of pictures for that to work 😄
Check out "Introducing Create ML".
Small tip: make sure to include some pictures of clear SFW-samples: Buildings, trees, cars, children playing, etc. Otherwise the model won't know if a landscape picture is SFW or not ;)

A screenshot of a porn scene with straight up nudity gets a confidence rating of 45%.

A fully dressed woman gets in a Thai traditional outfit gets 2%.

A fully dressed woman with Mickey Mouse ears gets 1%.

A fully dressed woman in a winter bubble jacket gets 0.01%.

How do you guys use this on your app and not deal with users leaving by the droves?

[yarodevuci]

@datureezy @absolute-heike I tried on pure naked body from front and back and it's like 0.5 -0.7 percent. nah would not use this in my app..

[dustturtle]

I think the createML created model have a kind of fault. The confidence rate is not so linear. The problem may be due to the design : extracting image feature and then a logistic regression. If you fine tuning a model, that problem will not occur.

[lukepighetti]

@datureezy set your threshold for 30%? Idk, just guessing

[yarodevuci]

does anyone has a better model ?

[lukepighetti]

No but seriously, what's the problem? If it's detecting them reliably at 30% then use 30% as your threshold.