README.md

metadata

license: apache-2.0
base_model:
  - google/siglip2-base-patch32-256
datasets:
  - strangerguardhf/DSE1
language:
  - en
pipeline_tag: image-classification
library_name: transformers
tags:
  - siglip2
  - '384'
  - explicit-content
  - adult-content
  - classification

siglip2-x256p32-explicit-content

siglip2-x256p32-explicit-content is a vision-language encoder model fine-tuned from siglip2-base-patch32-256 for multi-class image classification. Based on the SiglipForImageClassification architecture, this model is designed to detect and categorize various forms of visual content, from safe to explicit, making it ideal for content moderation and media filtering.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

---

Classification Report:
                     precision    recall  f1-score   support

      Anime Picture     0.9314    0.9139    0.9226      5600
     Hentai Picture     0.9349    0.9213    0.9281      4180
     Normal or Safe     0.9340    0.9328    0.9334      5503
        Pornography     0.9769    0.9650    0.9709      5600
Enticing or Sensual     0.9264    0.9663    0.9459      5600

           accuracy                         0.9409     26483
          macro avg     0.9407    0.9398    0.9402     26483
       weighted avg     0.9410    0.9409    0.9408     26483

---

Label Space: 5 Classes

This model classifies each image into one of the following content types:

Class 0: "Anime Picture"  
Class 1: "Hentai Picture"  
Class 2: "Normal or Safe"  
Class 3: "Pornography"  
Class 4: "Enticing or Sensual"

---

Install Dependencies

pip install -q transformers torch pillow gradio

---

Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/siglip2-x256p32-explicit-content"  # Replace with your HF 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 Picture",
    "2": "Normal or Safe",
    "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= "siglip2-x256p32-explicit-content",
    description="Classifies images as Anime, Hentai, Pornography, Enticing, or Safe for use in moderation systems."
)

if __name__ == "__main__":
    iface.launch()

---

Intended Use

This model is ideal for:

• AI-Powered Content Moderation
• NSFW and Explicit Media Detection
• Content Filtering in Social Media Platforms
• Image Dataset Cleaning & Annotation
• Parental Control Solutions