Instructions to use Nahrawy/AIorNot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nahrawy/AIorNot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Nahrawy/AIorNot") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Nahrawy/AIorNot") model = AutoModelForImageClassification.from_pretrained("Nahrawy/AIorNot") - Inference
- Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- competitions/aiornot
language:
- en
metrics:
- accuracy
tags:
- generative ai
- classification
Classification model used to classify real images and AI generated images.
The model used is swin-tiny-patch4-window7-224 finetued on aiornot dataset.
To use the model
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
labels = ["Real", "AI"]
feature_extractor = AutoFeatureExtractor.from_pretrained("Nahrawy/AIorNot")
model = AutoModelForImageClassification.from_pretrained("Nahrawy/AIorNot")
input = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**input)
logits = outputs.logits
prediction = logits.argmax(-1).item()
label = labels[prediction]