Instructions to use timm/efficientnet_el.ra_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_el.ra_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_el.ra_in1k", pretrained=True) - Transformers
How to use timm/efficientnet_el.ra_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_el.ra_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/efficientnet_el.ra_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36dced9f21b19ed264f506ffcf819c9c208190b686a535711702535a651b41bc
- Size of remote file:
- 42.9 MB
- SHA256:
- c671093e795b61c6d72c086d415b733d19a01ce8bd8d01512880bfde3cd7f0e4
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