Instructions to use timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_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_augreg_inat21 with timm:
import timm model = timm.create_model("hf-hub:timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21", pretrained=True) - Transformers
How to use timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_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_augreg_inat21") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 53ecf2d564973a8af5b79a370433963500847cc7f74d6196a627703504abdc35
- Size of remote file:
- 1.26 GB
- SHA256:
- 69518ce5a4944875a988f030a558489cfce9f2962282ce36701c8269f9d49cbe
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