Instructions to use timm/vit_base_patch8_224.augreg2_in21k_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch8_224.augreg2_in21k_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch8_224.augreg2_in21k_ft_in1k", pretrained=True) - Transformers
How to use timm/vit_base_patch8_224.augreg2_in21k_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_base_patch8_224.augreg2_in21k_ft_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_base_patch8_224.augreg2_in21k_ft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ca4328c2f2b44615dba46e8880b6b954e2ba97e6bb2476f246477bf901dacdd
- Size of remote file:
- 346 MB
- SHA256:
- 788b05f6178a60111324172978cb2a60502d9915fd9901dcde6652e9dbf28f18
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