Instructions to use timm/maxvit_tiny_tf_224.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/maxvit_tiny_tf_224.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/maxvit_tiny_tf_224.in1k", pretrained=True) - Transformers
How to use timm/maxvit_tiny_tf_224.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/maxvit_tiny_tf_224.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_tiny_tf_224.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 362002d8fcc9974748e4797f3fe88d57869cab992d6507f758d7a3f49045c957
- Size of remote file:
- 124 MB
- SHA256:
- bb7df98fcb8576411cd97b34fdbb418bed22710fbf60943ba943fe46d332ff8b
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