| | ---
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| | tags:
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| | - image-classification
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| | - pytorch
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| | - huggingface
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| | - vit
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| | - emotion-recognition
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| | datasets:
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| | - zenodo
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| | - mendeley
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| | - raf-db
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| | - affectnet
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| | base_model: trpakov/vit-face-expression
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| | library_name: transformers
|
| | ---
|
| |
|
| | # ViT Face Expression (Universal / Combined)
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| |
|
| | This model is a fine-tuned version of [trpakov/vit-face-expression](https://huggingface.co/trpakov/vit-face-expression) on a massive combined dataset including:
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| | - **Zenodo (IFEED)**
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| | - **Mendeley (GFFD-2025)**
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| | - **RAF-DB**
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| | - **AffectNet**
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| |
|
| | ## Model Description
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| | - **Architecture**: Vision Transformer (ViT)
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| | - **Task**: Facial Emotion Recognition
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| | - **Emotions**: Anger, Disgust, Fear, Happiness, Neutral, Sadness, Surprise
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| | - **Goal**: General-purpose robustness across varied domains (web images, lab settings, etc.)
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| |
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| | ## Usage
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| |
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| | ```python
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| | from transformers import ViTImageProcessor, ViTForImageClassification
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| | from PIL import Image
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| | import requests
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| |
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| | url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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| | image = Image.open(requests.get(url, stream=True).raw)
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| |
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| | repo_name = "michaelgathara/vit-face-universal"
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| |
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| | processor = ViTImageProcessor.from_pretrained(repo_name)
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| | model = ViTForImageClassification.from_pretrained(repo_name)
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| |
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| | inputs = processor(images=image, return_tensors="pt")
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| | outputs = model(**inputs)
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| | logits = outputs.logits
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| | # model predicts one of the 7 emotions
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| | predicted_class_idx = logits.argmax(-1).item()
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| | print("Predicted class:", model.config.id2label[predicted_class_idx])
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| | ```
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| |
|
