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prithivMLmods/vit-mini-explicit-content

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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vit-mini-explicit-content

vit-mini-explicit-content is an image classification vision-language model fine-tuned from vit-base-patch16-224-in21k for a single-label classification task. It categorizes images based on their explicitness using the ViTForImageClassification architecture.

\[!Note] This model is designed to promote safe, respectful, and responsible online spaces. It does not generate explicit content; it only classifies images. Misuse may violate platform or regional policies and is strongly discouraged.

[!Note]

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale : https://arxiv.org/abs/2010.11929, Visual Transformers: Token-based Image Representation and Processing for Computer Vision: https://arxiv.org/pdf/2006.03677

[!Important]

Note: Explicit, sensual, and pornographic content may appear in the results; however, all of them are considered not safe for work.

Classification Report:
                     precision    recall  f1-score   support

      Anime Picture     0.9077    0.7937    0.8469      5600
Extincing & Sensual     0.9245    0.9717    0.9475      5618
             Hentai     0.8680    0.9391    0.9021      5600
        Pornography     0.9614    0.9544    0.9579      5970
      Safe for Work     0.9235    0.9235    0.9235      6000

           accuracy                         0.9171     28788
          macro avg     0.9170    0.9165    0.9156     28788
       weighted avg     0.9177    0.9171    0.9163     28788

The model categorizes images into five classes:

—Class 0: Anime Picture
—Class 1: Enticing & Sensual
—Class 2: Hentai
—Class 3: Pornography
—Class 4: Safe for Work

Run with Transformers

python

!pip install -q transformers torch pillow gradio

python

import gradio as gr
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/vit-mini-explicit-content"  # Updated model path
model = ViTForImageClassification.from_pretrained(model_name)
processor = ViTImageProcessor.from_pretrained(model_name)

# Updated label mapping
labels = {
    "0": "Anime Picture",
    "1": "Enticing & Sensual",
    "2": "Hentai",
    "3": "Pornography",
    "4": "Safe for Work"
}

def explicit_content_detection(image):
    """Predicts the type of content in the 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()

    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    
    return predictions

# Create Gradio interface
iface = gr.Interface(
    fn=explicit_content_detection,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="vit-mini-explicit-content",
    description="Upload an image to classify whether it is anime, enticing & sensual, hentai, pornographic, or safe for work."
)

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