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--- |
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license: apache-2.0 |
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datasets: |
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- clip-benchmark/wds_fer2013 |
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base_model: |
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- microsoft/resnet-50 |
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pipeline_tag: image-classification |
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library_name: keras |
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--- |
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## Model Description |
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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. |
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This project focused on maximizing the performance of the pre-trained ResNet-50 architecture on this particularly challenging, noisy, and imbalanced dataset. |
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## Training Details |
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### Architecture |
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* **Base Model:** ResNet-50 (pre-trained on ImageNet). |
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* **Head:** Custom dense layers (224 units) with a high 0.5 dropout rate. |
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* **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. |
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### Optimization & Regularization |
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| Technique | Rationale | |
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| :--- | :--- | |
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| **Class Weighting** | Applied inverse frequency weights to mitigate the severe class imbalance (e.g., Disgust is rare, Happy is abundant). | |
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| **Data Augmentation** | Used random flips, translations, rotations, and zooms to artificially expand the small dataset and combat overfitting. | |
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| **High Dropout** | Increased dropout to 0.5 to aggressively regularize the model and prevent the divergence seen in earlier training runs. | |
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| **Optimizer** | Adam with a very low fine-tuning learning rate of 5e-6. | |
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## Evaluation Results |
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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. |
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### Overall Performance |
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| Metric | Result | |
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| :--- | :--- | |
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| **Test Accuracy** | **45.70\%** | |
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| **Test Loss** | 1.4929 | |
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| **Training Accuracy (End)** | 63.25\% | |
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### Per-Class F1-Scores |
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The F1-Score highlights the model's difficulty with ambiguous negative emotions. |
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| Emotion | F1-Score | Support (Test Count) | Notes | |
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| :--- | :--- | :--- | :--- | |
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| **Neutral** | **0.6386** | 831 | Highest precision, well-distinguished class. | |
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| **Happy** | 0.6037 | 1774 | Strongest recall, the most abundant class. | |
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| **Disgust** | 0.4659 | 111 | Significantly improved performance on this rare class. | |
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| **Sad** | $0.3995$ | 1233 | Ambiguous. | |
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| **Surprise** | 0.3531 | 1247 | Ambiguous. | |
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| **Fear** | 0.3374 | 1024 | Ambiguous. | |
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| **Angry** | **0.3312** | 958 | Lowest F1-score, indicating high confusion. | |
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## 💡 Usage and Limitations |
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### Inputs |
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* **Image Format:** Grayscale (48x48 pixels). |
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* **Normalization:** Pixel values must be scaled to [0, 1] (by dividing by 255.0). |
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### Recommended Libraries |
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* `tensorflow` (for loading the model) |
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* `numpy` (for array manipulation) |
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### Limitations |
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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. |
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2. **Overfitting:** Despite aggressive regularization, the model remains highly overfit (Training vs. Test gap), which is characteristic of this dataset. |
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### ❓ Troubleshooting the Error |
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If you encounter `ValueError` upon loading, ensure you are loading the model with the `.keras` extension: |
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```python |
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import tensorflow as tf |
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loaded_model = tf.keras.models.load_model("./best_fer_resnet_local/best_model.keras") |
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``` |
