Instructions to use timm/efficientnet_b5.sw_in12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_b5.sw_in12k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_b5.sw_in12k", pretrained=True) - Transformers
How to use timm/efficientnet_b5.sw_in12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_b5.sw_in12k") 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_b5.sw_in12k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5837ae62230a959cc9a80480dc7d160135721bd21c1314ba05267c2f7c02a09c
- Size of remote file:
- 211 MB
- SHA256:
- f270631f45a83703e0688a2452dab0494d5e15231db2339e6b1d6794867c5cea
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