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

- Xet hash:
- 01fd9aeee5a1795c74bc586af413db9b5a4ae432efcf6a352a08e06c63c67e2f
- Size of remote file:
- 9.38 kB
- SHA256:
- 61f75090fca06aacba8a55ea045458ad181a963ecf4f40cb069c44e7da0b3fde
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