Instructions to use timm/convnext_xlarge.fb_in22k_ft_in1k_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_xlarge.fb_in22k_ft_in1k_384 with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_xlarge.fb_in22k_ft_in1k_384", pretrained=True) - Transformers
How to use timm/convnext_xlarge.fb_in22k_ft_in1k_384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_xlarge.fb_in22k_ft_in1k_384") 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_xlarge.fb_in22k_ft_in1k_384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66dcc7a97f553379942781853a4f291ea4d8f2279712ff61015dacc44a0ec6cc
- Size of remote file:
- 1.4 GB
- SHA256:
- 12aae7dfb236b57d9a754cadb8d5be2886c80ead1f86e51e949740cd25967771
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