Instructions to use trpakov/vit-face-expression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trpakov/vit-face-expression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="trpakov/vit-face-expression") 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("trpakov/vit-face-expression") model = AutoModelForImageClassification.from_pretrained("trpakov/vit-face-expression") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
2428fbf
1
Parent(s): 24d2953
Adding `safetensors` variant of this model
Browse files- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:48acf03b1fd90ea45c5e91eb3be2f364cee1c1342639962bd4eae1eac2ad2f93
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size 343239356
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