Instructions to use nqvii/vit_fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_3") 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_3") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fed6fbb20f332982d93944c73228615aff3b50f22c5f419df24fb5a1e1760f1
- Size of remote file:
- 5.2 kB
- SHA256:
- afa693d59f03cdc3897014fa44cf0d6cfc70e1687bb948c5e90794b828095fc3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.