nsfw-detection-2-nano

NSFW Nudity and Sexual Content Classification

A mobile-friendly visual content moderation model, based on the work of F. C. Akyon obtained by fine-tuning EfficientNet-b0 on nsfw images to detect nudity and sexual content in images or video frames with high accuracy.

For a Demo, see: [viddexa/moderators]

Performance Comparison

Image detection performance of this model on 5 classes described above compared with other models trained by our team provided below with average and per-class F-scores.

<b><span style="color:turquoise">nsfw-detection-2-nano</span></b> and nsfw-detection-2-mini supports 5 classes: Normal, Hentai, Porn, Sexy, Drawing

• Normal: Photos without nsfw content.
• Porn: Photos with pornographic content.
• Hentai: Drawing with sexual content.
• Drawing: Comics, cartoons or drawings with no nsfw content.
• Sexy: Photos with no explicit nudity but might contain some characteristics of nsfw content such as suggestive nudity or risqué clothing.

<!-- ### Confusion Matrix Normalized confusion matrix of the model on test dataset: -->

Usage with moderators library

Install with pip install moderators then run:

from moderators import AutoModerator

model = AutoModerator.from_pretrained("viddexa/nsfw-detection-2-nano")
results = model("<path-to-image-file>")

probs = {k: v for r in results for k, v in r.classifications.items()}
predicted_label = max(probs, key=probs.get) 

Usage with transformers library

Install with pip install transformers then run:

from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification)
from PIL import Image
import torch

img = Image.open("<path-to-image-file>")

processor = AutoImageProcessor.from_pretrained("viddexa/nsfw-detection-2-nano", use_fast = False)
model=  AutoModelForImageClassification.from_pretrained("viddexa/nsfw-detection-2-nano")

with torch.no_grad():
    inputs = processor(images=img, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits
    probs = torch.softmax(logits, dim=-1)
    pred_id = int(probs.argmax())
    print(model.config.id2label[pred_id])

Model Details

<!-- Provide a longer summary of what this model is. -->

5-class nudity and sexual content classification model that classifies images as Normal, Porn, Hentai, Drawing or Sexy. Model is obtained by fine-tuning google's Efficientnet-b0.

• Developed by: [Kerem Bozgan, Abdullah Kırman, Fatih Çağatay Akyön]
• License: [apache-2.0]
• Finetuned from model: [google/efficientnet-b0]

Intended Use and Limitations

This model is designed to detect explicit nudity alone. For instance, an image containing suggestive nudity or risqué clothing is labeled as safe if there is no explicit nudity in the image.

Appropriate Use Cases:

• Filtering explicit images in user-generated content platforms
• Assisting moderation workflows for social apps and community forums
• Parental control or restricted content filtering
• Content review pipelines for games, and streaming services

In these settings, the model acts as an initial screening tool to help reduce moderator workload. A human review step is recommended for final decisions.

This model is not intended for:

• Moral or ethical judgment of individuals or their bodies <!-- Even though the model itself doesn’t make judgments: Someone could apply it to personal photos and claim it implies something about the individual (e.g., “this person is indecent” or “immoral”).-->
• Surveillance or monitoring of people in real-world environments
• Determining sexual intent <!-- For instance, cannot distinguish suggestive posing to label it nsfw-->
• Legal classification of pornography vs. art
• Use in contexts affecting employment, policing, social credit, or identity

BibTeX entry and citation info

@article{akyon2023nudity,
  title={State-of-the-art in nudity classification: A comparative analysis},
  author={Akyon, Fatih Cagatay and Temizel, Alptekin},
  booktitle={2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

Model Card Authors

Kerem Bozgan & Fatih Akyon