Instructions to use optimum/vit-base-patch16-224-neuronx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum/vit-base-patch16-224-neuronx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="optimum/vit-base-patch16-224-neuronx") 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("optimum/vit-base-patch16-224-neuronx") model = AutoModelForImageClassification.from_pretrained("optimum/vit-base-patch16-224-neuronx") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("optimum/vit-base-patch16-224-neuronx")
model = AutoModelForImageClassification.from_pretrained("optimum/vit-base-patch16-224-neuronx")Quick Links
Exported with
optimum-cli export neuron --model facebook/deit-base-patch16-224 --batch_size 1 --task image-classification vit_neuron/
- Downloads last month
- 2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="optimum/vit-base-patch16-224-neuronx") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")