Instructions to use HardlyHumans/Facial-expression-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HardlyHumans/Facial-expression-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HardlyHumans/Facial-expression-detection") 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("HardlyHumans/Facial-expression-detection") model = AutoModelForImageClassification.from_pretrained("HardlyHumans/Facial-expression-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition.
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It is trained on the FER2013and AffectNet datasets, which consist of facial images categorized into
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The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition.
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It is trained on the FER2013and AffectNet datasets, which consist of facial images categorized into eight different emotions:
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-anger
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-contempt
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-sad
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