How To Fine-Tune Models for Better NSFW AI Detection?

A forum thread on fine-tuning CLIP-based NSFW detection models surfaced practical guidance from practitioners, including a suggested starting point of batch size 16 and learning rate 5e-5 drawn from a ViT tuning document. Participants recommended prioritizing dataset quality and diversity over fine-tuning alone, testing multiple confidence thresholds instead of a fixed 0.5 cutoff, and evaluating false positives and false negatives separately on real, AI-generated, edited, resized, and compressed images. The discussion referenced NSFWJS and the LAION-AI CLIP-based NSFW Detector as existing tools.

Hey everyone, I 'm new here. Hope you guys don’t mind answering basic questions. I’ve been exploring NSFW AI detection lately, and it’s been a pretty fascinating rabbit hole. Tools like NSFWJS https://github.com/infinitered/nsfwjs are great for quick setups, and the CLIP-based NSFW Detector https://github.com/LAION-AI/CLIP-based-NSFW-Detector is super impressive with how it uses embeddings to classify content. Recently, I came across this site called soulfun.ai which is all about creative AI stuff including ai generated photos and videos , and it got me thinking: how can I fine-tune these models for more niche or specific datasets? I’ve been playing around with a basic CLIP setup, and here’s a quick snippet of what I’ve tried so far: python from transformers import CLIPProcessor, CLIPModel import torch Load the pre-trained CLIP model model = CLIPModel.from pretrained "openai/clip-vit-base-patch32" processor = CLIPProcessor.from pretrained "openai/clip-vit-base-patch32" Set up inputs inputs = processor text= "NSFW", "SFW" , images=image, return tensors="pt", padding=True Forward pass outputs = model inputs logits per image = outputs.logits per image Scores for image-text similarity probs = logits per image.softmax dim=1 Probabilities for each class Check if NSFW is nsfw = probs 0 0 0.5 Please let know, for those of you who’ve fine-tuned a CLIP-based model for NSFW or even something similar : - What kind of datasets worked best for you? - Did you use any specific tricks during training to improve accuracy? - Any tips for keeping the model fast and lightweight during inference? Would love to hear what’s worked for you Thanks in advance for any advice. I found a document that describes some of the parameters used during the tuning process, even though it is a ViT model rather than a CLIP model. The basic flow and libraries used are the same even when tuning a CLIP model. It’s just a different model class. Thanks, I’ll take a deep look The ViT model used a batch size of 16 and a learning rate of 5e-5. Do you think these parameters would be a good starting point for a CLIP model as well, or would adjustments be needed due to differences in model architecture? Anyway, thanks a lot I don’t have much experience training models, so I don’t really know However, since the image processing part of CLIP is ViT, I think it’s probably fine. Well, I think that the optimal values are something that you have to try and find out, so I think it’s more reliable to adjust them while actually training. hahaha, I should stop being lazy and try it for myself, thanks. Well, training models and optimizing them is really like alchemy, I guess. What other dataset do you use for such NSFW content detection apart from NSFWJS? Thanks One thing I’d focus on besides fine-tuning is the quality and diversity of the dataset. AI-generated images can look very different depending on the model, generation settings, editing, and compression, so a model trained on a narrow dataset can struggle with images it hasn’t seen before. I’d also test different confidence thresholds rather than relying on a fixed 0.5 cutoff. Check false positives and false negatives separately, especially for borderline images. For practical testing, can include both real and AI-generated images, then add edited, resized, and compressed versions to see how much the detection accuracy changes. That usually gives a better idea of how well the model will perform outside the training dataset.