Instructions to use timm/regnety_040.pycls_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnety_040.pycls_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/regnety_040.pycls_in1k", pretrained=True) - Transformers
How to use timm/regnety_040.pycls_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnety_040.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_040.pycls_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 351390689582e2df94c7bdafbb4b8352cc38da97c0ae434809cc9b969fe83878
- Size of remote file:
- 83 MB
- SHA256:
- b7a0a14cec1c30d9b876446b6b86fb67e020631e95e2f607355c755f0dae50f9
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