Instructions to use bn22/naflexvit_base_patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bn22/naflexvit_base_patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="bn22/naflexvit_base_patch16")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("bn22/naflexvit_base_patch16") model = AutoModel.from_pretrained("bn22/naflexvit_base_patch16") - timm
How to use bn22/naflexvit_base_patch16 with timm:
import timm model = timm.create_model("hf_hub:bn22/naflexvit_base_patch16", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83bce4cb6ecfd4ebe232b1fbf7b29f210646df17b5b09a31d8f59b014ada2376
- Size of remote file:
- 372 MB
- SHA256:
- 07c13d6d0f3b4ad6c41bea71be09c294d4be0c0a024a585bebfac744dd693059
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