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:
- f5060bdb6d360947d5c3c450d09bfa3d4ddd9204b4bba799375de7a336e8193e
- Size of remote file:
- 1.9 GB
- SHA256:
- 735a62a7af08af9208da4699701a0da2828b79dbb919cd63d301510d6f353ac2
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