Image-Guard-ckpt-3312
Image-Guard-ckpt-3312 is a multiclass image safety classification model fine-tuned from google/siglip2-base-patch16-224. This checkpoint is provided for experimental purposes. For production or actual usage, please refer to the final released models. It classifies images into multiple safety-related categories using the SiglipForImageClassification architecture.
Model Evaluation:
precision recall f1-score support
Anime-SFW 0.8696 0.8718 0.8707 5600
Hentai 0.9057 0.8567 0.8805 4180
Normal-SFW 0.8865 0.8726 0.8795 5503
Pornography 0.9451 0.9230 0.9340 5600
Enticing or Sensual 0.8705 0.9371 0.9026 5600
accuracy 0.8942 26483
macro avg 0.8955 0.8923 0.8934 26483
weighted avg 0.8950 0.8942 0.8942 26483
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. |
Install Dependencies
pip install -q transformers torch 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/Image-Guard-ckpt-3312"
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-ckpt-3312",
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()
Intended Use
Image-Guard-ckpt-3312 is designed for:
- Content Moderation – 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.
Note: This checkpoint is experimental. For production-grade usage, use the final verified model versions.
Limitations
- The model may misclassify borderline or artistically abstract images.
- It does not perform face recognition or identify individuals.
- Performance depends on lighting, resolution, and visual context.
- Human moderation is still recommended for sensitive content.