Instructions to use MatanBT/vit-base-patch16-224-celeba-smiling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatanBT/vit-base-patch16-224-celeba-smiling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MatanBT/vit-base-patch16-224-celeba-smiling") 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("MatanBT/vit-base-patch16-224-celeba-smiling") model = AutoModelForImageClassification.from_pretrained("MatanBT/vit-base-patch16-224-celeba-smiling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MatanBT/vit-base-patch16-224-celeba-smiling: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/MatanBT/vit-base-patch16-224-celeba-smiling/resolve/main/training_args.bin
- Command line
-
hf download hf://MatanBT/vit-base-patch16-224-celeba-smiling/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MatanBT/vit-base-patch16-224-celeba-smiling/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- bed39a6ceaefd32e8430fee988a65e50db6e43e8e9ba480eb0ae390d19423c40
- Size of remote file:
- 4.86 kB
- SHA256:
- 5aae73508b59ce639a7ac722aac55ee5c53e6cfc1d2457628c67ddbde3cb377d
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