Instructions to use timm/efficientvit_l2.r288_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientvit_l2.r288_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientvit_l2.r288_in1k", pretrained=True) - Transformers
How to use timm/efficientvit_l2.r288_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientvit_l2.r288_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_l2.r288_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4032def180817566533a8f1546ce05e6374b40df84832772f79182fb3a6d3870
- Size of remote file:
- 255 MB
- SHA256:
- 11d2c69ddfacd009ee1a26c2409a4afc85f5dfd9bdfaed679fc0451c107bdb82
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