ai_vs_real_image_detection_art

AI vs Real Art Detection (ViT)

This model is a Fine-Tuned Vision Transformer (ViT) designed to distinguish between Human-Created Artwork and AI-Generated Images.

It is based on google/vit-base-patch16-224-in21k and has been fine-tuned on a dataset of scraped web images containing digital art, paintings, and AI generations.

Model Details

• Model Architecture: Vision Transformer (ViT)
• Task: Binary Image Classification
• Classes: AI_GENERATED vs REAL
• Base Model: Google ViT (Patch 16, 224x224)

Performance Metrics

The model achieves an overall accuracy of 80.09%.

Detailed Classification Report

Confusion Matrix

Analysis

The model demonstrates a high recall for AI_GENERATED images (94.4%), meaning it is very effective at catching AI artwork. However, it has lower recall for REAL images (65.7%), suggesting that it is strict and occasionally misclassifies real artwork as AI-generated.

How to Use

You can use this model with the Hugging Face pipeline.

from transformers import pipeline

# Load the pipeline
pipe = pipeline("image-classification", model="Kowshik24/ai_vs_real_image_detection_art")

# Predict
image_path = "path_to_your_image.jpg"
result = pipe(image_path)

print(result)