ResNetFer_2013

Model Description

This model is a ResNet-50 deep convolutional neural network fine-tuned for the FER-2013 (Facial Expression Recognition 2013) dataset. The dataset consists of low-resolution (48x48) grayscale images of faces categorized into seven core emotional states.

This project focused on maximizing the performance of the pre-trained ResNet-50 architecture on this particularly challenging, noisy, and imbalanced dataset.

Training Details

Architecture

• Base Model: ResNet-50 (pre-trained on ImageNet).
• Head: Custom dense layers (224 units) with a high 0.5 dropout rate.
• Transfer Learning Strategy: Deep Freezing. The model base was frozen up to the conv5 block, meaning only the final convolutional block (conv5) and the custom head were fine-tuned. This prevents early layers, which are optimized for high-resolution images, from being corrupted by the 48x48 input.

Optimization & Regularization

Evaluation Results

The final model achieved its highest stability and best performance after 50 epochs of fine-tuning, demonstrating strong generalization given the difficulty of the data.

Overall Performance

Per-Class F1-Scores

The F1-Score highlights the model's difficulty with ambiguous negative emotions.

💡 Usage and Limitations

Inputs

• Image Format: Grayscale (48x48 pixels).
• Normalization: Pixel values must be scaled to [0, 1] (by dividing by 255.0).

Recommended Libraries

• tensorflow (for loading the model)
• numpy (for array manipulation)

Limitations

1. Low Accuracy: The 45.70% accuracy is limited by the low resolution (48x48) and noisy labels of the FER-2013 dataset. It is not comparable to modern human performance (65%-68% on FER-2013) or models trained on high-quality, high-resolution "in-the-wild" datasets like AffectNet.
2. Overfitting: Despite aggressive regularization, the model remains highly overfit (Training vs. Test gap), which is characteristic of this dataset.

❓ Troubleshooting the Error

If you encounter ValueError upon loading, ensure you are loading the model with the .keras extension:

import tensorflow as tf
loaded_model = tf.keras.models.load_model("./best_fer_resnet_local/best_model.keras")