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")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("metadome/face_shape_classification") model = AutoModelForImageClassification.from_pretrained("metadome/face_shape_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/Oblong.jpg from metadome/face_shape_classification: direct link, hf CLI and curl.
- Browser
- Download file 43.2 kB
-
https://huggingface.co/metadome/face_shape_classification/resolve/main/images/Oblong.jpg
- Command line
-
hf download hf://metadome/face_shape_classification/images/Oblong.jpg
-
curl -L -o Oblong.jpg https://huggingface.co/metadome/face_shape_classification/resolve/main/images/Oblong.jpg
43.2 kB

- Xet hash:
- 0135a2fbb80a37f5e0dc6b446f0aa2e551b6e9aef184824bd753dd54ad259905
- Size of remote file:
- 43.2 kB
- SHA256:
- 93cd3a3ca5f89adb875e26f5a3b73099d13eef765ccd2334db481dd3762690c2
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