Instructions to use Rob4343/FaceShapeAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Rob4343/FaceShapeAI with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Rob4343/FaceShapeAI", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| license: mit | |
| library_name: sklearn | |
| tags: | |
| - face-shape | |
| - image-classification | |
| - mediapipe | |
| - face-detection | |
| pipeline_tag: image-classification | |
| # Face Shape AI | |
| FaceShapeAI predicts your **face shape** from a photo. | |
| ### Try the app demo | |
| - [Face Shape Detector](https://attractivenesstest.com/face_shape) | |
| - [HF Space](https://huggingface.co/spaces/Rob4343/AIFaceShapeDetector). | |
|  | |
| ### Run locally | |
| - `python demo.py your_photo.jpg` | |
| FaceShapeAI analyzes your photo to detect a face and extract a dense set of facial landmarks (eyes, nose, mouth, jawline, and more). It then lightly normalizes the geometry—correcting small head tilt and scaling the landmarks for consistency across images. Finally, it runs the trained model included in this repo to predict the most likely face shape and prints confidence scores for each class. | |
| [GitHub Repository](https://github.com/rs75/FaceShapeAI) | |