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)# pip install -U transformers accelerate # 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
Download input.png from minchul/cvlface_DFA_resnet50: direct link, hf CLI and curl.
- Browser
- Download file 95.5 kB
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https://huggingface.co/minchul/cvlface_DFA_resnet50/resolve/main/input.png
- Command line
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hf download hf://minchul/cvlface_DFA_resnet50/input.png
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curl -L -o input.png https://huggingface.co/minchul/cvlface_DFA_resnet50/resolve/main/input.png
95.5 kB
