Image Classification
Transformers
Safetensors
English
siglip
siglip2
Not-For-All-Audiences
art
Synthetic
nsfw
sfw
Anime Picture
Hentai
Normal
Pornography
Enticing
Sensual
| license: apache-2.0 | |
| language: | |
| - en | |
| pipeline_tag: image-classification | |
| library_name: transformers | |
| tags: | |
| - siglip2 | |
| - not-for-all-audiences | |
| - art | |
| - synthetic | |
| - nsfw | |
| - sfw | |
| - Anime Picture | |
| - Hentai | |
| - Normal | |
| - Pornography | |
| - Enticing | |
| - Sensual | |
| datasets: | |
| - strangerguardhf/NSFW-MultiDomain-Classification | |
| base_model: | |
| - google/siglip2-base-patch16-224 | |
|  | |
| # **nsfw-image-detection** | |
| nsfw-image-detection 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. | |
| Original Model : https://huggingface.co/prithivMLmods/siglip2-x256-explicit-content | |
| --- | |
| ## **Evals** | |
| ```py | |
| Classification Report: | |
| precision recall f1-score support | |
| Anime Picture 0.8940 0.8718 0.8827 5600 | |
| Hentai 0.8961 0.8935 0.8948 4180 | |
| Normal 0.9100 0.8895 0.8997 5503 | |
| Pornography 0.9496 0.9654 0.9574 5600 | |
| Enticing or Sensual 0.9132 0.9429 0.9278 5600 | |
| accuracy 0.9137 26483 | |
| macro avg 0.9126 0.9126 0.9125 26483 | |
| weighted avg 0.9135 0.9137 0.9135 26483 | |
| ``` | |
|  | |
| --- | |
| # **Quick Start with Transformers🤗** | |
| ## **Install Dependencies** | |
| ```bash | |
| !pip install transformers torch pillow gradio | |
| ``` | |
| --- | |
| ## **Inference Code** | |
| ```python | |
| import gradio as gr | |
| from transformers import AutoImageProcessor, SiglipForImageClassification | |
| from PIL import Image | |
| import torch | |
| # Load model and processor | |
| model_name = "strangerguardhf/nsfw-image-detection" # Replace with your model path if needed | |
| model = SiglipForImageClassification.from_pretrained(model_name) | |
| processor = AutoImageProcessor.from_pretrained(model_name) | |
| # ID to Label mapping | |
| id2label = { | |
| "0": "Anime Picture", | |
| "1": "Hentai", | |
| "2": "Normal", | |
| "3": "Pornography", | |
| "4": "Enticing or Sensual" | |
| } | |
| def classify_explicit_content(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_explicit_content, | |
| inputs=gr.Image(type="numpy"), | |
| outputs=gr.Label(num_top_classes=5, label="Predicted Content Type"), | |
| title="nsfw-image-detection", | |
| description="Classifies images into explicit, suggestive, or safe categories (e.g., Hentai, Pornography, Normal)." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |
| ``` | |
| --- | |
| ## **Demo Inference** | |
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| --- | |
| The model classifies each image into one of the following content categories: | |
| ``` | |
| Class 0: "Anime Picture" | |
| Class 1: "Hentai" | |
| Class 2: "Normal" | |
| Class 3: "Pornography" | |
| Class 4: "Enticing or Sensual" | |
| ``` | |
| --- |