Instructions to use timm/efficientnet_b3.ra2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_b3.ra2_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/efficientnet_b3.ra2_in1k", pretrained=True) - Transformers
How to use timm/efficientnet_b3.ra2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_b3.ra2_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/efficientnet_b3.ra2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/efficientnet_b3.ra2_in1k: direct link, hf CLI and curl.
- Browser
- Download file 49.5 MB
-
https://huggingface.co/timm/efficientnet_b3.ra2_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/efficientnet_b3.ra2_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/efficientnet_b3.ra2_in1k/resolve/main/pytorch_model.bin
49.5 MB
- Xet hash:
- 9cb7b5f323de6ae94b7b047bc519da24135cdcdb15791629741f780fa4eb5195
- Size of remote file:
- 49.5 MB
- SHA256:
- 4862731817e56b83f538e76767de09caf38ce8345dcf90132c4c59fa6a0048ec
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