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---
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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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- affectnet
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base_model: trpakov/vit-face-expression
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library_name: transformers
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---
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# ViT Face Expression (Fine-tuned on AffectNet)
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This model is a fine-tuned version of [trpakov/vit-face-expression](https://huggingface.co/trpakov/vit-face-expression) on the [AffectNet](http://mohammadmahoor.com/affectnet/) dataset.
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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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## Dataset
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AffectNet is a large-scale database of facial expressions in the wild, containing more than 1M facial images from the Internet. This model was fine-tuned on a subset of the manually annotated images covering 7 basic emotions (excluding Contempt to align with the base model's taxonomy).
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## Usage
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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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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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repo_name = "michaelgathara/vit-face-affectnet"
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processor = ViTImageProcessor.from_pretrained(repo_name)
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model = ViTForImageClassification.from_pretrained(repo_name)
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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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