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
File size: 402 Bytes
f66d5ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"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
}
|