Instructions to use timm/regnety_160.pycls_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnety_160.pycls_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/regnety_160.pycls_in1k", pretrained=True) - Transformers
How to use timm/regnety_160.pycls_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnety_160.pycls_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/regnety_160.pycls_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fda05e2c427fa9b00803f78a72f04317eff70c6de931ec026081cb276c7f2dec
- Size of remote file:
- 335 MB
- SHA256:
- 26ae43af3fe79ae4f13877309854bec013ca4606396f39178882756dcdb654a2
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