Instructions to use nqvii/vit_fold_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_4") 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_4") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e80656659a80397f5cefc20c40bc8f8517aadeaf94cb549144d044e9150deff
- Size of remote file:
- 5.2 kB
- SHA256:
- d61a50751fe5069dab9c2f25b5ade49712bfac3acdcd3b3204adfb87e4894384
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