Instructions to use timm/efficientvit_b3.r224_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientvit_b3.r224_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientvit_b3.r224_in1k", pretrained=True) - Transformers
How to use timm/efficientvit_b3.r224_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientvit_b3.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_b3.r224_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0575d5e90ebd38f8b1e2da4f040e37a0d359ea9d151d38c0c578f79a006b4f24
- Size of remote file:
- 195 MB
- SHA256:
- b732ebbeead92dbdce1a7bdfc3b4c1e575a8c2f2cd6d5d26479425592c1eaea5
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