I am new to neural networks, I've only started studying and learning about the subject a year ago, and I just started building my first neural network.
The project is a little bit ambitious: A browser extension for children's safety, it checks for sexual or abusive content, so that it replaces that content with a placeholder, the user will have to insert a password to show original content.
I didn't find a dataset online, so I decided to build my training dataset. So, I started by writing a web crawler, it starts collecting images, meanwhile implementing data augmentation techniques. It basically resizes images (to 95x95), crops them, rotates, changes colors, adds blur, black and white, noise, etc.
The problem is that after applying these techniques, I noticed that some images are not even recognizable by a human subject.
I mean that even though I know that picture contains sexual content, it doesn't even appear to be sexual anymore.
So, do I have to label it as sexual or not sexual?
Notice that it's easier for me to consider it as sexual, if every image produces about 50 edited images, I'd only have to label the original image, what follows is that all 50 images get the same label. Is it okay to do just that?
This is a sample of what I get after doing data augmentation, notice that some pictures are not recognizable by humans.
For example, look at the result after editing images hue and saturation, a human can't recognize this result, is it okay to label it: not sexual?
I wouldn't recognize the picture on the right if I didn't see the original one.
I also tested this on human subjects (my brothers), they didn't recognize the squirrel on the right.