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--- |
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license: apache-2.0 |
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datasets: |
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- strangerguardhf/NSFW-MultiDomain-Classification-v2.0 |
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language: |
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- en |
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base_model: |
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- google/vit-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- explicit-content-detection |
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- mini |
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- art |
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- sensual-content-detection |
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- Anime |
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--- |
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# **vit-mini-explicit-content** |
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> **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. |
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> \[!Note] |
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> 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. |
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> [!Note] |
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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 |
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> [!Important] |
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Note: Explicit, sensual, and pornographic content may appear in the results; however, all of them are considered not safe for work. |
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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 0.9077 0.7937 0.8469 5600 |
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Extincing & Sensual 0.9245 0.9717 0.9475 5618 |
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Hentai 0.8680 0.9391 0.9021 5600 |
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Pornography 0.9614 0.9544 0.9579 5970 |
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Safe for Work 0.9235 0.9235 0.9235 6000 |
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accuracy 0.9171 28788 |
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macro avg 0.9170 0.9165 0.9156 28788 |
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weighted avg 0.9177 0.9171 0.9163 28788 |
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``` |
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--- |
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The model categorizes images into five classes: |
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* **Class 0:** Anime Picture |
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* **Class 1:** Enticing & Sensual |
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* **Class 2:** Hentai |
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* **Class 3:** Pornography |
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* **Class 4:** Safe for Work |
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# **Run with Transformers** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import ViTImageProcessor, ViTForImageClassification |
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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/vit-mini-explicit-content" # Updated model path |
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model = ViTForImageClassification.from_pretrained(model_name) |
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processor = ViTImageProcessor.from_pretrained(model_name) |
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# Updated label mapping |
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labels = { |
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"0": "Anime Picture", |
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"1": "Enticing & Sensual", |
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"2": "Hentai", |
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"3": "Pornography", |
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"4": "Safe for Work" |
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} |
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def explicit_content_detection(image): |
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"""Predicts the type of content in the 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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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=explicit_content_detection, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="vit-mini-explicit-content", |
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description="Upload an image to classify whether it is anime, enticing & sensual, hentai, pornographic, or safe for work." |
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) |
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# Launch the app |
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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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## Demo Inference |
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> [!warning] |
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Anime Picture |
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> [!warning] |
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Extincing & Sensual |
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> [!warning] |
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Hentai |
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> [!warning] |
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Pornography |
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> [!warning] |
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Safe for Work |
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--- |
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# **Recommended Use Cases** |
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* Image moderation pipelines |
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* Parental and institutional content filters |
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* Dataset cleansing before training |
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* Online safety and well-being platforms |
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* Enhancing search engine filtering |
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# **Discouraged / Prohibited Use** |
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* Non-consensual or malicious monitoring |
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* Automated judgments without human review |
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* Misrepresentation of moderation systems |
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* Use in unlawful or unethical surveillance |
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* Harassment, exploitation, or shaming |
