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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-256 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- siglip2 |
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- '256' |
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- patch16 |
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- adult-content-detection |
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- explicit-content-detection |
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--- |
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# **siglip2-x256-explicit-content** |
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> **siglip2-x256-explicit-content** is a vision-language encoder model fine-tuned from **siglip2-base-patch16-256** for **multi-class image classification**. Built on the **SiglipForImageClassification** architecture, the model is trained to identify and categorize content types in images, especially for **explicit, suggestive, or safe media filtering**. |
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> [!note] |
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*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786 |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Anime Picture 0.8940 0.8718 0.8827 5600 |
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Hentai 0.8961 0.8935 0.8948 4180 |
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Normal 0.9100 0.8895 0.8997 5503 |
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Pornography 0.9496 0.9654 0.9574 5600 |
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Enticing or Sensual 0.9132 0.9429 0.9278 5600 |
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accuracy 0.9137 26483 |
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macro avg 0.9126 0.9126 0.9125 26483 |
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weighted avg 0.9135 0.9137 0.9135 26483 |
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``` |
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--- |
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## **Label Space: 5 Classes** |
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The model classifies each image into one of the following content categories: |
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``` |
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Class 0: "Anime Picture" |
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Class 1: "Hentai" |
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Class 2: "Normal" |
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Class 3: "Pornography" |
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Class 4: "Enticing or Sensual" |
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``` |
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--- |
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## **Install Dependencies** |
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```bash |
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pip install -q transformers torch pillow gradio |
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``` |
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--- |
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## **Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/siglip2-x256-explicit-content" # Replace with your model path if needed |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# ID to Label mapping |
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id2label = { |
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"0": "Anime Picture", |
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"1": "Hentai", |
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"2": "Normal", |
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"3": "Pornography", |
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"4": "Enticing or Sensual" |
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} |
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def classify_explicit_content(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_explicit_content, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Predicted Content Type"), |
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title="siglip2-x256-explicit-content", |
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description="Classifies images into explicit, suggestive, or safe categories (e.g., Hentai, Pornography, Normal)." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Intended Use** |
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This model is intended for applications such as: |
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- **Content Moderation**: Automatically detect NSFW or suggestive content. |
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- **Parental Controls**: Enable AI-based filtering for safe media browsing. |
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- **Dataset Preprocessing**: Clean and categorize image datasets for research or deployment. |
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- **Online Platforms**: Help enforce content guidelines for uploads and user-generated media. |
