Instructions to use t1msan/ViT_generated_or_not with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use t1msan/ViT_generated_or_not with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="t1msan/ViT_generated_or_not") 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("t1msan/ViT_generated_or_not") model = AutoModelForImageClassification.from_pretrained("t1msan/ViT_generated_or_not") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("t1msan/ViT_generated_or_not")
model = AutoModelForImageClassification.from_pretrained("t1msan/ViT_generated_or_not")Quick Links
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="t1msan/ViT_generated_or_not") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")