Instructions to use nqvii/vit_fold_3_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/vit_fold_3_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/vit_fold_3_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_3_v3") model = AutoModelForImageClassification.from_pretrained("nqvii/vit_fold_3_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0434a974a082b0968b5c41c5e3e5de9a6335b32d328caf8c50234ec8a60633d
- Size of remote file:
- 5.2 kB
- SHA256:
- c64146ca5e8a6fdda79b49dfaa5fa68e039f6438d9fec839787e31117f9e01f6
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