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OuiAhmed/
ResNetFer_2013

Name: ResNetFer_2013
Availability: InStock

keras

image-classification

clip-benchmark/wds_fer2013

microsoft/resnet-50

finetune

apache-2.0

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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

Technique	Rationale
Class Weighting	Applied inverse frequency weights to mitigate the severe class imbalance (e.g., Disgust is rare, Happy is abundant).
Data Augmentation	Used random flips, translations, rotations, and zooms to artificially expand the small dataset and combat overfitting.
High Dropout	Increased dropout to 0.5 to aggressively regularize the model and prevent the divergence seen in earlier training runs.
Optimizer	Adam with a very low fine-tuning learning rate of 5e-6.

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

Metric	Result
Test Accuracy	45.70%
Test Loss	1.4929
Training Accuracy (End)	63.25%

Per-Class F1-Scores

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

Emotion	F1-Score	Support (Test Count)	Notes
Neutral	0.6386	831	Highest precision, well-distinguished class.
Happy	0.6037	1774	Strongest recall, the most abundant class.
Disgust	0.4659	111	Significantly improved performance on this rare class.
Sad	$0.3995$	1233	Ambiguous.
Surprise	0.3531	1247	Ambiguous.
Fear	0.3374	1024	Ambiguous.
Angry	0.3312	958	Lowest F1-score, indicating high confusion.

💡 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

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.
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:

python

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

OuiAhmed/ResNetFer_2013