Instructions to use nqvii/vit_fold_4_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_4_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_4_v3") 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_v3") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_4_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cca4d977ac2a69db7f7b41022a62d62e4f4b6e53afe06c8cf0814d25b0009860
- Size of remote file:
- 5.2 kB
- SHA256:
- bbe1228475e8df4e79a93fc4d16a6817e7b8d2f6c4f29ac10a39ef2d819fb35e
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