Instructions to use timm/hrnet_w64.ms_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/hrnet_w64.ms_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/hrnet_w64.ms_in1k", pretrained=True) - Transformers
How to use timm/hrnet_w64.ms_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/hrnet_w64.ms_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/hrnet_w64.ms_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b83067dd0d85961de45c2918503e80e9bd0568cb0a51bb328652767216590769
- Size of remote file:
- 513 MB
- SHA256:
- 36e3c9bf6550668284bde0114812485fd510b206035f87f744ace22cc9a35434
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