Facial Expression Detection

🚀 Facial-Expression-Recognition

This model is designed for facial expression recognition, fine - tuned from a pre - trained model. It solves the problem of accurately classifying facial emotions in images, providing high - accuracy results on relevant datasets.

🚀 Quick Start

For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 Doc / guide: https://huggingface.co/docs/hub/model - cards

Example usage

from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline

pipe = pipeline("image - classification", model="HardlyHumans/Facial - expression - detection")

processor = AutoImageProcessor.from_pretrained("HardlyHumans/Facial - expression - detection")
model = AutoModelForImageClassification.from_pretrained("HardlyHumans/Facial - expression - detection")

labels = model.config.id2label

outputs = model(**inputs)

logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
predicted_label = labels[predicted_class_idx]

✨ Features

This model is a fine - tuned version of google/vit - base - patch16 - 224 - in21k on the FER 2013 and AffectNet dataset. It achieves an accuracy of 0.922 and a loss of 0.213 on the evaluation set.

Model Description

The vit - face - expression model is a Vision Transformer fine - tuned for facial emotion recognition. It is trained on the FER2013 and AffectNet datasets, which contain facial images classified into eight different emotions:

• anger
• contempt
• sad
• happy
• neutral
• disgust
• fear
• surprise

📚 Documentation

Model Details

The model has been fine - tuned using the following hyperparameters:

Model Architecture and Objective

The model is based on the Vision Transformer architecture and is fine - tuned for facial emotion recognition.

🔧 Technical Details

The net estimated CO2 emission using the Machine Learning Impact calculator scale is around 8.82kg of CO2.