Instructions to use timm/maxvit_large_tf_224.in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/maxvit_large_tf_224.in21k with timm:
import timm model = timm.create_model("hf_hub:timm/maxvit_large_tf_224.in21k", pretrained=True) - Transformers
How to use timm/maxvit_large_tf_224.in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/maxvit_large_tf_224.in21k") 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_large_tf_224.in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2168e4b57e685ba37409071632039986d5d0fb0b943f123ada7443f3a689ac1d
- Size of remote file:
- 934 MB
- SHA256:
- 68f443327205b4a3daf529049c57c8103586b60e0ecb42ac6b0a53a729dfe2a5
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