Instructions to use timm/vit_large_patch14_clip_336.laion2b_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.laion2b_ft_augreg_inat21 with timm:
import timm model = timm.create_model("hf_hub:timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21", pretrained=True) - Transformers
How to use timm/vit_large_patch14_clip_336.laion2b_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.laion2b_ft_augreg_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.laion2b_ft_augreg_inat21", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4406e4e930ce407b64c48b875e15f7892b1bf1808b8fcdbf436df4eefa174b5f
- Size of remote file:
- 1.26 GB
- SHA256:
- 100333a791489e84aab399837e365601129963b13fa65a4904132442b482c5c8
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