Instructions to use timm/vit_huge_patch14_clip_378.dfn5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_huge_patch14_clip_378.dfn5b with timm:
import timm model = timm.create_model("hf_hub:timm/vit_huge_patch14_clip_378.dfn5b", pretrained=True) - Transformers
How to use timm/vit_huge_patch14_clip_378.dfn5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_huge_patch14_clip_378.dfn5b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_huge_patch14_clip_378.dfn5b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card for vit_huge_patch14_clip_378.dfn5b
timm DFN CLIP (image encoder only) weights from https://huggingface.co/apple/DFN5B-CLIP-ViT-H-14-378
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