Instructions to use nqvii/vit_fold_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_2") 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("nqvii/vit_fold_2") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4855ed18e8d69d139f97bf48063ec2b89c0567e8860fdffb656dda8ef9a7aa0
- Size of remote file:
- 5.2 kB
- SHA256:
- a9af8e2594582777cb91923194dba435dfe890cdff6c0a98d332d1f25e8a7c44
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