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---
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.
> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
---
```py
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**
```bash
pip install -q transformers torch pillow gradio
```
---
## **Inference Code**
```python
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** |