Instructions to use minchul/cvlface_adaface_vit_base_webface4m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minchul/cvlface_adaface_vit_base_webface4m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="minchul/cvlface_adaface_vit_base_webface4m", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("minchul/cvlface_adaface_vit_base_webface4m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ed0cec0a944a332bd7da0520b19de383300027192e13853766b0279cf54e703
- Size of remote file:
- 460 MB
- SHA256:
- 5fafd6b7d599a3ede5fac5bd1d01ad05e9e93e89b39b7687d4a3bc93ff2aebc0
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