Update README.md

README.md

@@ -7,18 +7,9 @@ base_model:
 pipeline_tag: image-classification
 library_name: keras
 ---
-
-That's a fantastic final step\! A clear **README** is essential for your Hugging Face page to explain the model's purpose, performance, and usage, especially since you optimized a challenging transfer learning task.
-
-Here is a comprehensive README template based on your final results and methodology.
-
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-
-# Model Card: ResNet-50 Fine-Tuned for FER-2013 Facial Expression Recognition
-
 ## 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 ($48 \times 48$) grayscale images of faces categorized into seven core emotional states.
+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.
 
@@ -27,8 +18,8 @@ This project focused on maximizing the performance of the pre-trained ResNet-50
 ### Architecture
 
   * **Base Model:** ResNet-50 (pre-trained on ImageNet).
-  * **Head:** Custom dense layers ($224$ units) with a high $\mathbf{0.5}$ 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 $48 \times 48$ input.
+  * **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
 
@@ -36,8 +27,8 @@ This project focused on maximizing the performance of the pre-trained ResNet-50
 | :--- | :--- |
 | **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 $\mathbf{0.5}$ 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 $5e-6$. |
+| **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
 
@@ -47,9 +38,9 @@ The final model achieved its **highest stability and best performance** after 50
 
 | Metric | Result |
 | :--- | :--- |
-| **Test Accuracy** | **$45.70\%$** |
-| **Test Loss** | $1.4929$ |
-| **Training Accuracy (End)** | $63.25\%$ |
+| **Test Accuracy** | **45.70\%** |
+| **Test Loss** | 1.4929 |
+| **Training Accuracy (End)** | 63.25\% |
 
 ### Per-Class F1-Scores
 
@@ -58,19 +49,19 @@ 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. |
+| **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. |
+| **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 ($48 \times 48$ pixels).
-  * **Normalization:** Pixel values must be scaled to $\mathbf{[0, 1]}$ (by dividing by $255.0$).
+  * **Image Format:** Grayscale (48x48 pixels).
+  * **Normalization:** Pixel values must be scaled to [0, 1] (by dividing by 255.0).
 
 ### Recommended Libraries
 
@@ -79,7 +70,7 @@ The F1-Score highlights the model's difficulty with ambiguous negative emotions.
 
 ### Limitations
 
-1.  **Low Accuracy:** The $45.70\%$ accuracy is limited by the **low resolution** ($48 \times 48$) and **noisy labels** of the FER-2013 dataset. It is not comparable to modern human performance ($\approx 65\%-68\%$ on FER-2013) or models trained on high-quality, high-resolution "in-the-wild" datasets like AffectNet.
+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