10E Affecthq Fer Balanced W0.1 Jitter Jiggle
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10E Affecthq Fer Balanced W0.1 Jitter Jiggle
Developed by Piro17
A vision model fine-tuned from google/vit-base-patch16-224-in21k for facial expression recognition tasks
Downloads 17
Release Time : 2/15/2023
Model Overview
This model is a facial expression recognition model based on the ViT architecture. After fine-tuning on an unknown dataset, it achieved approximately 60% accuracy on the evaluation set.
Model Features
Balanced training
Utilized balanced training strategies, possibly employing class weights or data augmentation techniques
Data augmentation
Used data augmentation techniques such as jitter and jiggle during training
Stable training
Through 10 epochs of training, the model's performance improved steadily without overfitting
Model Capabilities
Facial expression recognition
Image classification
Use Cases
Emotion analysis
Real-time expression recognition
Can be used for real-time expression analysis in video conferences or social media
Accuracy approximately 60%
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