Instructions to use timm/efficientnet_b2.ra_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_b2.ra_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_b2.ra_in1k", pretrained=True) - Transformers
How to use timm/efficientnet_b2.ra_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_b2.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_b2.ra_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbe6441613c7b9385104a1d7d68bf31c96a314a674842180b28c9d0374e13fed
- Size of remote file:
- 36.9 MB
- SHA256:
- 27f82b064eab49b87e272a0fbf5aa95ce69a3eb9f1bf7685b968d2d1ae5fa532
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