Instructions to use timm/vit_base_patch32_clip_224.openai_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch32_clip_224.openai_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch32_clip_224.openai_ft_in1k", pretrained=True) - Transformers
How to use timm/vit_base_patch32_clip_224.openai_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_base_patch32_clip_224.openai_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_patch32_clip_224.openai_ft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61827d956fb9403392a9b4e52cb3b961c288cb7091f7c45c36ced9edcacc5af9
- Size of remote file:
- 353 MB
- SHA256:
- aaed61f5f2b474048ef4030063b68e21925d135a87d0633e973e91bd32205860
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