Instructions to use timm/vit_base_patch16_clip_224.openai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch16_clip_224.openai with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch16_clip_224.openai", pretrained=True) - Transformers
How to use timm/vit_base_patch16_clip_224.openai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch16_clip_224.openai")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch16_clip_224.openai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93695c181535284061d1c255e997bce909ba722f0c830b680d13b84a966db527
- Size of remote file:
- 599 MB
- SHA256:
- 53b401837e5baa82115d3b6db60c6fff43cfd1a6412e48ed0193d54a69d2f993
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