Instructions to use timm/repghostnet_150.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/repghostnet_150.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/repghostnet_150.in1k", pretrained=True) - Transformers
How to use timm/repghostnet_150.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/repghostnet_150.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_150.in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 340b3ea48414af4620acd4211e5390379751d4af67c8c2d65084d46b38cd0c5d
- Size of remote file:
- 26.7 MB
- SHA256:
- e02989e92d822998ed1ad5398e6fcc36f72c5609f062483198928bb5a0aad16b
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