Instructions to use timm/convnext_base.clip_laion2b_augreg_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_base.clip_laion2b_augreg_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_base.clip_laion2b_augreg_ft_in1k", pretrained=True) - Transformers
How to use timm/convnext_base.clip_laion2b_augreg_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_base.clip_laion2b_augreg_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/convnext_base.clip_laion2b_augreg_ft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77666e09753a33ef39c353c620ba63b7d168613095b930d00f84a197859ab4eb
- Size of remote file:
- 354 MB
- SHA256:
- 704857e9af8c0fab865670efbb204e4cee0b08e203d341c81352de3d8c79c8a6
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