Instructions to use Thao2202/vit-Facial-Expression-Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thao2202/vit-Facial-Expression-Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Thao2202/vit-Facial-Expression-Recognition") 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("Thao2202/vit-Facial-Expression-Recognition") model = AutoModelForImageClassification.from_pretrained("Thao2202/vit-Facial-Expression-Recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41d018093239077b6c5474d929018e6de8bfd49482c3726e433c7fb185ea9be0
- Size of remote file:
- 343 MB
- SHA256:
- f3fa58786c9203186224534100ba50338d2cad26ff317573ee37a886edfc9f1a
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