Instructions to use timm/repghostnet_080.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/repghostnet_080.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/repghostnet_080.in1k", pretrained=True) - Transformers
How to use timm/repghostnet_080.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/repghostnet_080.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/repghostnet_080.in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f1141abf3637a3921f3871918d5444816fe1e90f819e6897ce4b1f8b3de5ca9
- Size of remote file:
- 13.5 MB
- SHA256:
- be529d0c95ed2d8ca10791c329d2f30ddcc0e49a434d834d99408ad07e574e9a
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