Enhance Facial Expression Recognition Model with EfficientNet and Advanced Techniques

[bhanushri12]

@sanjay-kv I would like to raise this issue as a part of the GSSoC'24 The current model architecture for facial expression recognition is based on convolutional neural networks (CNNs) designed for image classification tasks. While leveraging pre-trained models from the TIMM library has been effective, there are several ways to further enhance performance and reduce training time. Proposed Improvements

1. Optimizing the Training Process:

   • Apply regularization techniques like dropout and weight decay to prevent overfitting.
   • Use mixed precision training to enhance training speed and reduce memory usage.
   • Implement differential learning rates for different layers of the network.

2. Data Augmentation: -Implement a more diverse range of data augmentations to improve model robustness and generalization.

   • Sample augmentations include horizontal flip, rotation, brightness/contrast adjustment, random cropping, Gaussian noise, and Gaussian blur.

I would like to work on this issue as I have already been working with face datasets.

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[bhanushri12]

@sanjay-kv can I start working on this issue?

[sanjay-kv]

yes