Instructions to use minchul/cvlface_adaface_ir101_webface4m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minchul/cvlface_adaface_ir101_webface4m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="minchul/cvlface_adaface_ir101_webface4m", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("minchul/cvlface_adaface_ir101_webface4m", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- wrapper.py +2 -2
wrapper.py
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@@ -23,8 +23,8 @@ class CVLFaceRecognitionModel(PreTrainedModel):
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self.model = get_model(model_conf)
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self.model.load_state_dict_from_path('pretrained_model/model.pt')
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def forward(self,
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return self.model(
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self.model = get_model(model_conf)
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self.model.load_state_dict_from_path('pretrained_model/model.pt')
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def forward(self, *args, **kwargs):
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return self.model(*args, **kwargs)
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