Instructions to use minchul/cvlface_DFA_resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minchul/cvlface_DFA_resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="minchul/cvlface_DFA_resnet50", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("minchul/cvlface_DFA_resnet50", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c3b75074f2d0efd03b5e53cd6a3984e0f5969d78b62316dba6fb65317788711
- Size of remote file:
- 110 MB
- SHA256:
- a4a0d74aa3d3d4f3da2e8dc34c7ac4596cec1bb1994f3f9e2a99bf61bd5416cc
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