Instructions to use timm/vit_large_patch14_clip_336.datacompxl_ft_inat21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_large_patch14_clip_336.datacompxl_ft_inat21 with timm:
import timm model = timm.create_model("hf_hub:timm/vit_large_patch14_clip_336.datacompxl_ft_inat21", pretrained=True) - Transformers
How to use timm/vit_large_patch14_clip_336.datacompxl_ft_inat21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_large_patch14_clip_336.datacompxl_ft_inat21") 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_patch14_clip_336.datacompxl_ft_inat21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8f0ef44271bed8fa02b369cd660196fa7aede6881f2cea83fffdaee34c51aab
- Size of remote file:
- 1.26 GB
- SHA256:
- 6d7ed6600dcc2be9ee4d10a5a0ade26a3bbb5eb117ff758e1ee4e862a801d3fd
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