Instructions to use nqvii/vit_fold_2_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_2_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_2_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_2_v3") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_2_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a418bf7604f261de4dd27aa5125eb0d5d2fe04b733146f48bdd0adaff78e2b59
- Size of remote file:
- 5.2 kB
- SHA256:
- c8757cd384502a263c61b7f67a3783363f3296b1373afbeafcfebfe6f3ecd800
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.