Instructions to use timm/regnetz_d8.ra3_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnetz_d8.ra3_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/regnetz_d8.ra3_in1k", pretrained=True) - Transformers
How to use timm/regnetz_d8.ra3_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnetz_d8.ra3_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/regnetz_d8.ra3_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbe9fccd514a70534b490adcd1c351a3058cabfffff85491e8fa088a88b359f5
- Size of remote file:
- 94.1 MB
- SHA256:
- 485875df3f9f4b729e149f8ec755ec8c6054828748555e9ed51175cdcd9f394a
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