Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use metadome/face_shape_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use metadome/face_shape_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="metadome/face_shape_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("metadome/face_shape_classification") model = AutoModelForImageClassification.from_pretrained("metadome/face_shape_classification") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("metadome/face_shape_classification")
model = AutoModelForImageClassification.from_pretrained("metadome/face_shape_classification")Quick Links
face_shape_classification
Autogenerated by HuggingPics๐ค๐ผ๏ธ
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
Heart
Oblong
Oval
Round
Square
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fadhlaoui1212/faceanalyzershape
Evaluation results
- Accuracyself-reported0.853





# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="metadome/face_shape_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")