Instructions to use ashercn97/isaface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ashercn97/isaface with timm:
import timm model = timm.create_model("hf_hub:ashercn97/isaface", pretrained=True) - Transformers
How to use ashercn97/isaface with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ashercn97/isaface") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ashercn97/isaface", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36ec89b7937d76335dc8a2e772084638938f752e651eb539e41cda9c9a82bdcb
- Size of remote file:
- 43.3 MB
- SHA256:
- d53759fcdab5f3cd6c1bb2f7604a04e16f2c4595fb2cb502fac8506edd9fab67
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