Instructions to use nqvii/vit_fold_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_5") 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_5") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63ff9ed6fb20ba5b59628b20e8bc8d236d0d084b519a7efbf6fff61d22e661fa
- Size of remote file:
- 5.2 kB
- SHA256:
- 2339ae6a0ad437e50dc00fe8d6a7e65acfa32b913eda3492000dbddc89b187fa
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