Instructions to use timm/tf_efficientnet_b8.ra_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tf_efficientnet_b8.ra_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/tf_efficientnet_b8.ra_in1k", pretrained=True) - Transformers
How to use timm/tf_efficientnet_b8.ra_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tf_efficientnet_b8.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/tf_efficientnet_b8.ra_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d9db8d06428f23b094cf4f5565699b4613d5e8741aba8a74eeae38d4fc760dd
- Size of remote file:
- 352 MB
- SHA256:
- ced9fe11f8d187183f5d7f904c0010f2f08b4e2144ea6dd7e7c3b2d0a31a2aca
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