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--- |
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license: apache-2.0 |
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base_model: |
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- google/siglip2-base-patch32-256 |
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datasets: |
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- strangerguardhf/DSE1 |
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language: |
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- en |
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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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- '384' |
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- explicit-content |
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- adult-content |
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- classification |
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--- |
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# **siglip2-x256p32-explicit-content** |
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> **siglip2-x256p32-explicit-content** is a vision-language encoder model fine-tuned from **siglip2-base-patch32-256** for **multi-class image classification**. Based on the **SiglipForImageClassification** architecture, this model is designed to detect and categorize various forms of visual content, from safe to explicit, making it ideal for content moderation and 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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--- |
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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 0.9314 0.9139 0.9226 5600 |
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Hentai Picture 0.9349 0.9213 0.9281 4180 |
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Normal or Safe 0.9340 0.9328 0.9334 5503 |
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Pornography 0.9769 0.9650 0.9709 5600 |
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Enticing or Sensual 0.9264 0.9663 0.9459 5600 |
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accuracy 0.9409 26483 |
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macro avg 0.9407 0.9398 0.9402 26483 |
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weighted avg 0.9410 0.9409 0.9408 26483 |
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``` |
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--- |
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## **Label Space: 5 Classes** |
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This model classifies each image into one of the following content types: |
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``` |
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Class 0: "Anime Picture" |
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Class 1: "Hentai Picture" |
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Class 2: "Normal or Safe" |
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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-x256p32-explicit-content" # Replace with your HF 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 Picture", |
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"2": "Normal or Safe", |
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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-x256p32-explicit-content", |
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description="Classifies images as Anime, Hentai, Pornography, Enticing, or Safe for use in moderation systems." |
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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 ideal for: |
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- **AI-Powered Content Moderation** |
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- **NSFW and Explicit Media Detection** |
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- **Content Filtering in Social Media Platforms** |
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- **Image Dataset Cleaning & Annotation** |
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- **Parental Control Solutions** |
