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.