NSFW Image Detection Model

A fine-tuned Vision Transformer (ViT) model for NSFW image classification based on the "google/vit-base-patch16-224-in21k" architecture.

Model Description

This model is a variant of the transformer encoder architecture adapted for image classification tasks. It has been fine-tuned specifically for detecting Not Safe For Work (NSFW) content in images with high accuracy.

Training Details

Base Model: google/vit-base-patch16-224-in21k
Image Resolution: 224x224 pixels
Batch Size: 16
Learning Rate: 5e-5
Training Dataset: 80,000 images (proprietary dataset)
Classes: 2 (normal, nsfw)

Training Stats

Evaluation Loss: 0.07463
Accuracy: 98.04%
Evaluation Runtime: 304.98s
Samples per Second: 52.46
Steps per Second: 3.28

How to Use

Using Pipeline (Simple Method)

from PIL import Image
from transformers import pipeline

img = Image.open("<path_to_image_file>")
classifier = pipeline("image-classification", model="ashishupadhyay/NSFW_DETECTION")
classifier(img)

Using Model Directly

import torch
from PIL import Image
from transformers import AutoModelForImageClassification, ViTImageProcessor

img = Image.open("<path_to_image_file>")
model = AutoModelForImageClassification.from_pretrained("ashishupadhyay/NSFW_DETECTION")
processor = ViTImageProcessor.from_pretrained('ashishupadhyay/NSFW_DETECTION')

with torch.no_grad():
    inputs = processor(images=img, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits

predicted_label = logits.argmax(-1).item()
model.config.id2label[predicted_label]

Intended Uses & Limitations

Intended Uses

NSFW Image Classification
Content Moderation
Safe Content Filtering

Limitations

Specialized for NSFW detection only
Performance may vary for other tasks
Should be used in conjunction with content guidelines and regulations

Important Notes

The model is fine-tuned specifically for NSFW content detection
Use responsibly and ethically
Adhere to applicable content guidelines and regulations
Performance may vary based on input image quality and characteristics

References

Disclaimer

The model's performance may be influenced by the quality and representativeness of the training data. Users should assess the model's suitability for their specific applications and datasets before implementation.