Instructions to use ashercn97/isaface-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ashercn97/isaface-v4 with timm:
import timm model = timm.create_model("hf_hub:ashercn97/isaface-v4", pretrained=True) - Transformers
How to use ashercn97/isaface-v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ashercn97/isaface-v4") 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-v4", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbbcdfc50e8b199af1a698699e86141eafee67cc57f7ed53b9cd5f21a22d411b
- Size of remote file:
- 6.21 MB
- SHA256:
- 3c0c7315895f578214e872f175bc463bef8b4cf0ff8cf064c6321c6d49f34ed8
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