Instructions to use timm/vit_small_patch16_224.augreg_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_small_patch16_224.augreg_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_small_patch16_224.augreg_in1k", pretrained=True) - Transformers
How to use timm/vit_small_patch16_224.augreg_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_small_patch16_224.augreg_in1k") 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_small_patch16_224.augreg_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c842384b6264b7e7b3124e4dc8b6170f1614819f7a00f079df55c467bdaca8e5
- Size of remote file:
- 88.3 MB
- SHA256:
- f7172d12b19350a6f2cf38abaf66f3055bff192105d26b7d5bdfdbed2f9bb4eb
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