# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/ai_vs_human_generated_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/ai_vs_human_generated_image_detection")Note to users who want to use this model in production
Beware that this model is trained on a dataset collected about 1 year ago. Since then, there is a remarkable progress in generating deepfake images with common AI tools, resulting in a significant concept drift. To mitigate that, I urge you to retrain the model using the latest available labeled data. As a quick-fix approach, simple reducing the threshold (say from default 0.5 to 0.1 or even 0.01) of labelling image as a fake may suffice. However, you will do that at your own risk, and retraining the model is the better way of handling the concept drift.
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Predicts with about 98% accuracy whether an attached image is AI-generated.
See https://www.kaggle.com/code/dima806/ai-vs-human-generated-images-prediction-vit for details.
Classification report:
precision recall f1-score support
human 0.9655 0.9930 0.9790 3998
AI-generated 0.9928 0.9645 0.9784 3997
accuracy 0.9787 7995
macro avg 0.9791 0.9787 0.9787 7995
weighted avg 0.9791 0.9787 0.9787 7995
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/ai_vs_human_generated_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")