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
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_edgeface.EdgeFaceImageProcessor" | |
| }, | |
| "do_align": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "EdgeFaceImageProcessor", | |
| "image_size": 112, | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "mp_backend": "auto", | |
| "rescale_factor": 0.00392156862745098 | |
| } | |