Instructions to use timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k", pretrained=True) - Transformers
How to use timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_base_patch16_clip_384.laion2b_ft_in12k_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_patch16_clip_384.laion2b_ft_in12k_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c0346186aa4dfa3d2e01016381bdda5079a77150053098835e0f9da72149d32
- Size of remote file:
- 347 MB
- SHA256:
- 5ec0e60a842ee8726c58608467d7bf31631bc89646c45decc91511a9b96d8bf5
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