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:
- 28295bcda8c7888936020314dbd2c6036a7b638ecc0ada0f8c27d63153733a4b
- Size of remote file:
- 599 MB
- SHA256:
- 52bdba61c0cb19e034fc891b15e6f15291d4c06dcceae69b772776c7bc9f8b35
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