![10.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/JKD_pyH8zyKU1-_eqo6mx.png)

#  **Guard-Against-Unsafe-Content-Siglip2**  

> **Guard-Against-Unsafe-Content-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to detect NSFW content, including vulgarity and nudity, using the SiglipForImageClassification architecture.  

> [!Important]
> Accuracy : 99 [ nudity, vulgarity, explicit content]

The model categorizes images into two classes:  
- **Class 0:** "Unsafe Content" – indicating that the image contains vulgarity, nudity, or explicit content.  
- **Class 1:** "Safe Content" – indicating that the image is appropriate and does not contain any unsafe elements.  

# **Run with Transformers🤗**

```python
!pip install -q transformers torch pillow gradio
```

```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Guard-Against-Unsafe-Content-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def nsfw_detection(image):
    """Predicts NSFW probability scores for an 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()
    
    labels = model.config.id2label
    predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
    
    return predictions

# Create Gradio interface
iface = gr.Interface(
    fn=nsfw_detection,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="NSFW Content Detection"),
    title="NSFW Image Detection",
    description="Upload an image to check if it contains unsafe content such as vulgarity or nudity."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()
```

    TrainOutput(global_step=376, training_loss=0.11756020403922872, metrics={'train_runtime': 597.6963, 'train_samples_per_second': 20.077, 'train_steps_per_second': 0.629, 'total_flos': 1.005065949855744e+18, 'train_loss': 0.11756020403922872, 'epoch': 2.0})

# **Intended Use:**  

The **Guard-Against-Unsafe-Content-Siglip2** model is designed to detect **inappropriate and explicit content** in images. It helps distinguish between **safe** and **unsafe** images based on the presence of **vulgarity, nudity, or other NSFW elements**.  

### Potential Use Cases:  
- **NSFW Content Detection:** Identifying images containing explicit content to help filter inappropriate material.  
- **Content Moderation:** Assisting platforms in filtering out unsafe images before they are shared publicly.  
- **Parental Controls:** Enabling automated filtering of explicit images in child-friendly environments.  
- **Safe Image Classification:** Helping AI-powered applications distinguish between safe and unsafe content for appropriate usage.
safe unsafe