huggan/wikiart
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This model is a specialized binary classifier trained to detect images generated by Stable Diffusion XL (SDXL). It achieves 99.60% accuracy on held-out test data.
Test Accuracy: 0.9960
Precision: 0.9930
Recall: 0.9990
F1 Score: 0.9960
AUC-ROC: 0.9999
False Positive Rate: 0.0070
False Negative Rate: 0.0010
import torch
from PIL import Image
from transformers import ViTForImageClassification, ViTImageProcessor
# Load model and processor
model = ViTForImageClassification.from_pretrained(
"ash12321/sdxl-detector-vit"
)
processor = ViTImageProcessor.from_pretrained(
"google/vit-base-patch16-224"
)
# Load image
image = Image.open("test.jpg")
inputs = processor(images=image, return_tensors="pt")
# Get prediction
model.eval()
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
if probs[0][1] > 0.5:
print(f"SDXL-Generated ({probs[0][1]:.2%} confident)")
else:
print(f"Real Image ({probs[0][0]:.2%} confident)")
from model import detect_image
result = detect_image("test.jpg", model_path="ash12321/sdxl-detector-vit")
print(f"Is Fake: {result['is_fake']}")
print(f"Confidence: {result['confidence']:.2%}")
pytorch_model.bin - Model weightsconfig.json - Model configurationmodel.py - Model architecture and helper functionsREADME.md - This documentationtraining_results.json - Detailed training metricstraining_curves.png - Training visualizationconfusion_matrix.png - Test set confusion matrix@misc{sdxl-detector-vit,
author = {ash12321},
title = {SDXL Detector - Vision Transformer},
year = {2024},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/ash12321/sdxl-detector-vit}},
}
License: Apache 2.0
Created: 2025-12-31