Image Feature Extraction
Transformers
Safetensors
timm
edgeface
feature-extraction
face-recognition
face-verification
face-embedding
custom_code
Instructions to use anjith2006/edgeface with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anjith2006/edgeface with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="anjith2006/edgeface", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anjith2006/edgeface", trust_remote_code=True, device_map="auto") - timm
How to use anjith2006/edgeface with timm:
import timm model = timm.create_model("hf_hub:anjith2006/edgeface", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Update edgeface-base/modeling_edgeface.py
Browse files
edgeface-base/modeling_edgeface.py
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@@ -58,6 +58,7 @@ class EdgeFaceOutput(ModelOutput):
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class EdgeFaceModel(PreTrainedModel):
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config_class = EdgeFaceConfig
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main_input_name = "pixel_values"
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def __init__(self, config: EdgeFaceConfig):
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super().__init__(config)
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class EdgeFaceModel(PreTrainedModel):
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config_class = EdgeFaceConfig
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main_input_name = "pixel_values"
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input_modalities="image"
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def __init__(self, config: EdgeFaceConfig):
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super().__init__(config)
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