Instructions to use timm/maxvit_xlarge_tf_512.in21k_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/maxvit_xlarge_tf_512.in21k_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/maxvit_xlarge_tf_512.in21k_ft_in1k", pretrained=True) - Transformers
How to use timm/maxvit_xlarge_tf_512.in21k_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/maxvit_xlarge_tf_512.in21k_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/maxvit_xlarge_tf_512.in21k_ft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b97851d22ba2a4174021e7d0327e6814f556c1f5e038c5e13e19e4dae583fa9f
- Size of remote file:
- 1.9 GB
- SHA256:
- 4be4da77d9af508dc003c3dc8f35220c968e772bbee6663bd15360471707228a
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