prithivMLmods/SigLIP2-ImageShield-90M-256

SigLIP2-ImageShield-90M-256

SigLIP2-ImageShield-90M-256 is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-256 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.

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

[!note] 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-256"  # 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-256",
    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

[image]

Precision / Recall / F1-score per Class

[image]

Confusion Matrix

[image]

Test Set Class Distribution

[image]

Overall Prediction Accuracy

[image]

Misalignment Distribution by True Class

[image]

Acknowledgements

• —[Transformers](https://huggingface.co/docs/transformers/en/index): 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](https://huggingface.co/papers/2502.14786): Multilingual vision-language encoders with improved semantic understanding, localization, and dense feature representations.