Instructions to use timm/vit_large_patch16_224.augreg_in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_large_patch16_224.augreg_in21k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_large_patch16_224.augreg_in21k", pretrained=True) - Transformers
How to use timm/vit_large_patch16_224.augreg_in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_large_patch16_224.augreg_in21k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_large_patch16_224.augreg_in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcf0076a5befcb7d269caad4f074588f9ec041802435a81a673c82d37dbdc068
- Size of remote file:
- 1.3 GB
- SHA256:
- 8964714579e301f5894f0cc98fc4e95713716515055f96203968b5591c73a032
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