siglip2-mini-explicit-content
siglip2-mini-explicit-content is an image classification vision-language encoder model fine-tuned from
siglip2-base-patch16-512
for a single-label classification task. It is designed to classify images into categories related to explicit, sensual, or safe-for-work content using the SiglipForImageClassification architecture.
This model is intended to promote positive, safe, and respectful digital environments. Misuse is strongly discouraged and may violate platform or regional guidelines. As a classification model, it does not generate unsafe content and is suitable for moderation purposes.
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786
Note: Explicit, sensual, and pornographic content may appear in the results; however, all of them are considered not safe for work.
Classification Report:
precision recall f1-score support
Anime Picture 0.8897 0.8296 0.8586 5600
Extincing & Sensual 0.8984 0.9477 0.9224 5618
Hentai 0.8993 0.9118 0.9055 5600
Pornography 0.9527 0.9285 0.9404 5970
Safe for Work 0.8957 0.9172 0.9063 6000
accuracy 0.9074 28788
macro avg 0.9072 0.9069 0.9066 28788
weighted avg 0.9076 0.9074 0.9071 28788
The model categorizes images into five classes:
- Class 0: Anime Picture
- Class 1: Extincing & Sensual
- Class 2: Hentai
- Class 3: Pornography
- Class 4: Safe for Work
Run with Transformers
!pip install -q transformers torch pillow gradio
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/siglip2-mini-explicit-content"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated labels
labels = {
"0": "Anime Picture",
"1": "Extincing & Sensual",
"2": "Hentai",
"3": "Pornography",
"4": "Safe for Work"
}
def detect_explicit_content(image):
"""Predicts content category in an uploaded 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()
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Gradio Interface
iface = gr.Interface(
fn=detect_explicit_content,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="siglip2-mini-explicit-content",
description="Upload an image to classify it as Anime, Hentai, Sensual, Pornographic, or Safe for Work."
)
if __name__ == "__main__":
iface.launch()
Guidelines for Use of siglip2-mini-explicit-content
This model is designed for responsible content moderation and filtering. It is especially tuned for anime, hentai, and adult content. Use it ethically, with the following guidelines:
Recommended Use Cases
- Content Moderation in social media and forums
- Parental Controls for safer browsing environments
- Dataset Curation for removing NSFW images from training data
- Safe Search Filtering for engines and discovery systems
- Workplace Image Scanning for compliance
Prohibited or Discouraged Use
- Harassment, exposure, or targeting of individuals
- Use on private content without consent
- Illegal or unethical surveillance
- Sole reliance for legal or reputational decisions
- Deceptive manipulation of moderation results
Important Notes
- Optimized for anime and adult content detection. Not suitable for detecting violence, drugs, or hate symbols.
- Probabilistic outputs — always verify with human review where needed.
- This model's predictions are not legal classifications.
Demo Inference
Anime Picture
Extincing & Sensual
Hentai
Pornography
Safe for Work
Ethical Reminder
This tool was created to enhance digital safety. Do not use it to harm, surveil, or exploit individuals or communities. By using this model, you commit to ethical and privacy-respecting practices.
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Model tree for prithivMLmods/siglip2-mini-explicit-content
Base model
google/siglip2-base-patch16-512