Instructions to use timm/efficientvit_m0.r224_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientvit_m0.r224_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientvit_m0.r224_in1k", pretrained=True) - Transformers
How to use timm/efficientvit_m0.r224_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientvit_m0.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_m0.r224_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 521082f3a4860b53f8248ba14f793ba5eb0d596caf2349123d42f7846dfd4ad4
- Size of remote file:
- 9.76 MB
- SHA256:
- df58d63000cd3619c9c3ac5a38fa0f69e051ec2520a4c1abfeba614f87287780
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