Instructions to use timm/efficientvit_b2.r224_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientvit_b2.r224_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientvit_b2.r224_in1k", pretrained=True) - Transformers
How to use timm/efficientvit_b2.r224_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientvit_b2.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_b2.r224_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3662c92e5cd40f0948af23f4117419d304501b20c690010ed3664fa80912496c
- Size of remote file:
- 97.6 MB
- SHA256:
- 15074c61bfa8253ef0f2b9a063dea61a71ff3f1bb1644733ad99181b837877fe
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