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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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+
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+ # ViT Face Expression (Fine-tuned on AffectNet)
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+
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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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+
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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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+
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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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+
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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-affectnet"
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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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+ "id2label": {
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+ "0": "anger",
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+ "1": "disgust",
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+ "2": "fear",
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+ "3": "happiness",
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+ "4": "neutral",
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+ "5": "sadness",
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+ "6": "surprise"
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+ },
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+ "anger": "0",
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+ "disgust": "1",
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+ "fear": "2",
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+ "happiness": "3",
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+ "neutral": "4",
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+ "sadness": "5",
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+ "surprise": "6"
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+ "transformers_version": "4.57.6"
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+ }
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