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, dtype="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
| { | |
| "architectures": [ | |
| "EdgeFaceModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_edgeface.EdgeFaceConfig", | |
| "AutoModel": "modeling_edgeface.EdgeFaceModel" | |
| }, | |
| "dtype": "float32", | |
| "featdim": 512, | |
| "low_rank_ratio": 0.6, | |
| "model_type": "edgeface", | |
| "timm_model": "edgenext_base", | |
| "transformers_version": "5.12.1", | |
| "use_low_rank": false | |
| } | |