nsfw-image-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-256 for multi-class image classification. Built on the SiglipForImageClassification architecture, the model is trained to identify and categorize content types in images, especially for explicit, suggestive, or safe media filtering.
Original Model : https://huggingface.co/prithivMLmods/siglip2-x256-explicit-content
Classification Report:
precision recall f1-score support
Anime Picture 0.8940 0.8718 0.8827 5600
Hentai 0.8961 0.8935 0.8948 4180
Normal 0.9100 0.8895 0.8997 5503
Pornography 0.9496 0.9654 0.9574 5600
Enticing or Sensual 0.9132 0.9429 0.9278 5600
accuracy 0.9137 26483
macro avg 0.9126 0.9126 0.9125 26483
weighted avg 0.9135 0.9137 0.9135 26483
!pip install transformers torch pillow gradio hf_xetimport gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "strangerguardhf/nsfw_image_detection" # Replace with your model path if needed
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# ID to Label mapping
id2label = {
"0": "Anime Picture",
"1": "Hentai",
"2": "Normal",
"3": "Pornography",
"4": "Enticing or Sensual"
}
def classify_explicit_content(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_explicit_content,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=5, label="Predicted Content Type"),
title="nsfw-image-detection",
description="Classifies images into explicit, suggestive, or safe categories (e.g., Hentai, Pornography, Normal)."
)
if __name__ == "__main__":
iface.launch()
The model classifies each image into one of the following content categories:
Class 0: "Anime Picture"
Class 1: "Hentai"
Class 2: "Normal"
Class 3: "Pornography"
Class 4: "Enticing or Sensual"
Several models have been trained using the NSFW_MultiDomain dataset:
Dataset Used : https://huggingface.co/datasets/strangerguardhf/NSFW-MultiDomain
- 🔗 https://huggingface.co/prithivMLmods/Mature-Content-Detection
- 🔗 https://huggingface.co/prithivMLmods/siglip2-x256-explicit-content
- 🔗 https://huggingface.co/prithivMLmods/siglip2-x256p32-explicit-content
- 🔗 https://huggingface.co/strangerguardhf/nsfw_image_detector
These models are fine-tuned on various architectures including SigLIP variants and custom classifiers for NSFW detection.