Instructions to use timm/efficientvit_m3.r224_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientvit_m3.r224_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientvit_m3.r224_in1k", pretrained=True) - Transformers
How to use timm/efficientvit_m3.r224_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientvit_m3.r224_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/efficientvit_m3.r224_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4867d889347ce489133cd8075a97b9966a2333c95f7ae1a0828c885a7ce683f6
- Size of remote file:
- 28 MB
- SHA256:
- e221e24c70f9e80ccb290cce0be9ee64df068bb3255588885e14956f9c60bb65
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