Image-Guard-2.0-Post0.1

Image-Guard-2.0-Post0.1 is a multiclass image safety classification model fine-tuned from google/siglip2-base-patch16-224. It classifies images into multiple safety-related categories using the SiglipForImageClassification architecture.

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-SFW     0.8906    0.8766    0.8835      5600
             Hentai     0.9081    0.8892    0.8986      4180
         Normal-SFW     0.9010    0.8784    0.8896      5503
        Pornography     0.9489    0.9448    0.9469      5600
Enticing or Sensual     0.8900    0.9436    0.9160      5600

           accuracy                         0.9076     26483
          macro avg     0.9077    0.9065    0.9069     26483
       weighted avg     0.9077    0.9076    0.9074     26483

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Label Space: 5 Classes

Class ID	Label	Description
0	Anime-SFW	Safe-for-work anime-style images.
1	Hentai	Explicit or adult anime content.
2	Normal-SFW	Realistic or photographic images that are safe for work.
3	Pornography	Explicit adult content involving nudity or sexual acts.
4	Enticing or Sensual	Suggestive imagery that is not explicit but intended to evoke sensuality.

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This model is experimental and may or may not be considered for actual use.

Install Dependencies

pip install -q transformers torch pillow gradio

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Inference Code

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

# Load model and processor
model_name = "prithivMLmods/Image-Guard-2.0-Post0.1"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
    "0": "Anime-SFW",
    "1": "Hentai",
    "2": "Normal-SFW",
    "3": "Pornography",
    "4": "Enticing or Sensual"
}

def classify_image_safety(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_safety,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=5, label="Image Safety Classification"),
    title="Image-Guard-2.0-Post0.1",
    description="Upload an image to classify it into one of five safety categories: Anime-SFW, Hentai, Normal-SFW, Pornography, or Enticing/Sensual."
)

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

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Intended Use

Image-Guard-2.0-Post0.1 is designed for:

• Content Moderation – Automatically identify and filter sensitive or NSFW imagery.
• Dataset Curation – Separate clean and explicit data for research and training.
• Platform Safety – Support compliance for social, educational, and media-sharing platforms.
• AI Model Input Filtering – Prevent unsafe data from entering multimodal or generative pipelines.

Limitations

• The model may occasionally misclassify borderline or artistically abstract images.
• It does not perform face recognition or identify individuals.
• Results depend on lighting, resolution, and visual context.
• The model does not replace human moderation for sensitive environments.