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:
- 78b196d45b0fa9d80ea2ec457a437ca0ad246e05628a8fc39e6d3b90e0a1293b
- Size of remote file:
- 460 MB
- SHA256:
- b184fa1f6cab821027b13f17fe86e5f19c661c134879f735f6fa0c03961e0b20
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