Instructions to use timm/hrnet_w32.ms_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/hrnet_w32.ms_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/hrnet_w32.ms_in1k", pretrained=True) - Transformers
How to use timm/hrnet_w32.ms_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/hrnet_w32.ms_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/hrnet_w32.ms_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/hrnet_w32.ms_in1k: direct link, hf CLI and curl.
- Browser
- Download file 166 MB
-
https://huggingface.co/timm/hrnet_w32.ms_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/hrnet_w32.ms_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/hrnet_w32.ms_in1k/resolve/main/pytorch_model.bin
166 MB
- Xet hash:
- b97a5267d2babbf094bfe7bdd9ea9f56af0fe994373e947d37d1bb0dcbc09eb0
- Size of remote file:
- 166 MB
- SHA256:
- ac470bfc377fbb279a5849a40885835ee589f0a8d0220319e222e9615ff9ae63
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