Instructions to use minchul/cvlface_adaface_ir50_ms1mv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minchul/cvlface_adaface_ir50_ms1mv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="minchul/cvlface_adaface_ir50_ms1mv2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("minchul/cvlface_adaface_ir50_ms1mv2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 477 Bytes
79c667c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"architectures": [
"CVLFaceRecognitionModel"
],
"auto_map": {
"AutoConfig": "wrapper.ModelConfig",
"AutoModel": "wrapper.CVLFaceRecognitionModel"
},
"conf": {
"color_space": "RGB",
"freeze": false,
"input_size": [
3,
112,
112
],
"name": "ir50",
"output_dim": 512,
"start_from": "",
"yaml_path": "models/iresnet/configs/v1_ir50.yaml"
},
"torch_dtype": "float32",
"transformers_version": "4.33.0"
}
|