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
| input_size: | |
| - 3 | |
| - 112 | |
| - 112 | |
| color_space: RGB | |
| name: ir50 | |
| output_dim: 512 | |
| start_from: '' | |
| freeze: false | |
| yaml_path: models/iresnet/configs/v1_ir50.yaml | |