SigLIP2-ImageShield-90M-224

SigLIP2-ImageShield-90M-224 is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. Built on the SiglipForImageClassification architecture, the model is designed to identify and categorize visual content for explicit, suggestive, and safe media filtering.

This model is experimental. Expert multimodal models are available here: ImageShield Multimodal SFT Collection.

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

Label Space: 5 Classes

The model classifies each image into one of the following content categories:

Class 0: "Anime"
Class 1: "Hentai"
Class 2: "Normal"
Class 3: "Pornography"
Class 4: "Sensual"

Install Dependencies

pip install transformers torch torchvision 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-ImageShield-90M-224"  # 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",
    "1": "Hentai",
    "2": "Normal",
    "3": "Pornography",
    "4": "Sensual"
}

def classify_image(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_image,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(
        num_top_classes=5,
        label="Predicted Content Type"
    ),
    title="SigLIP2-ImageShield-90M",
    description="Classifies images into Anime, Hentai, Normal, Pornography, and Sensual categories."
)

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

Intended Use

This model is intended for applications such as:

• Content Moderation: Detect explicit or suggestive visual content.
• Parental Controls: Support AI-based media filtering.
• Dataset Preprocessing: Categorize and filter image datasets.
• Online Platforms: Assist with content safety and upload moderation.

Classification Report

Training vs Evaluation Loss / Accuracy

Precision / Recall / F1-score per Class

Confusion Matrix

Test Set Class Distribution

Overall Prediction Accuracy

Misalignment Distribution by True Class

Acknowledgements

• Transformers: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.

• SigLIP 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense feature representations.