Instructions to use timm/regnetv_040.ra3_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnetv_040.ra3_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/regnetv_040.ra3_in1k", pretrained=True) - Transformers
How to use timm/regnetv_040.ra3_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnetv_040.ra3_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 AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/regnetv_040.ra3_in1k") model = AutoModelForImageClassification.from_pretrained("timm/regnetv_040.ra3_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/regnetv_040.ra3_in1k: direct link, hf CLI and curl.
- Browser
- Download file 83 MB
-
https://huggingface.co/timm/regnetv_040.ra3_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/regnetv_040.ra3_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/regnetv_040.ra3_in1k/resolve/main/pytorch_model.bin
83 MB
- Xet hash:
- 717bf35eb9b547edd3b79f637752b50d2b2c2e2c77cb7740a4a175ed3a6e9d9c
- Size of remote file:
- 83 MB
- SHA256:
- c2977b3cdebcff6a184b5d018a22c54f924e9142134d4d0d229a33c3b2df934a
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