Instructions to use ashercn97/isaface-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ashercn97/isaface-v2 with timm:
import timm model = timm.create_model("hf_hub:ashercn97/isaface-v2", pretrained=True) - Transformers
How to use ashercn97/isaface-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ashercn97/isaface-v2") 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-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3b0e67f55740dcf3d7bde38cb0ba12fa9cb36c1a3b78a6860c2ca80751a911b
- Size of remote file:
- 71.9 MB
- SHA256:
- 0e88678bd218aaf815556f7dd09eac972dadcbf47c510c5bdc00148cda39ccdd
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