Instructions to use timm/efficientnet_el_pruned.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_el_pruned.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_el_pruned.in1k", pretrained=True) - Transformers
How to use timm/efficientnet_el_pruned.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_el_pruned.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_pruned.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2245fc18827b0bf55b96ec2aa7d24a9ed0360245d30914b81934b9a2552b7e4
- Size of remote file:
- 42.9 MB
- SHA256:
- 94a01ec0e44ad0887a1bb148fb1fb449a26d07070607f87cc9c0228ac88e6c37
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