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---
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license: mit
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library_name: keras
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tags:
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- image-classification
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- keras
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- face
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- face-expression
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- facial-expression
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---
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# Facial expression classification model
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This model is designed for facial expression classification and it uses custom CNN model to classify the images into 7 different categories.
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This CNN Model is to classify the facial expression into one of the following categories:
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1. Anger
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2. Disgust
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3. Fear
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4. Happiness
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5. Neutral
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6. Sadness
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7. Surprise
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## Architecture Summary
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| Layer (type) | Output Shape | Param # |
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|--------------------------------------|-----------------------------|-----------------|
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| conv2d (Conv2D) | (None, 46, 46, 32) | 320 |
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| max_pooling2d (MaxPooling2D) | (None, 23, 23, 32) | 0 |
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| dropout (Dropout) | (None, 23, 23, 32) | 0 |
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| conv2d_1 (Conv2D) | (None, 21, 21, 64) | 18,496 |
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| max_pooling2d_1 (MaxPooling2D) | (None, 10, 10, 64) | 0 |
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| batch_normalization (BatchNormalization) | (None, 10, 10, 64) | 256 |
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| dropout_1 (Dropout) | (None, 10, 10, 64) | 0 |
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| conv2d_2 (Conv2D) | (None, 8, 8, 128) | 73,856 |
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| max_pooling2d_2 (MaxPooling2D) | (None, 4, 4, 128) | 0 |
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| batch_normalization_1 (BatchNormalization) | (None, 4, 4, 128) | 512 |
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| dropout_2 (Dropout) | (None, 4, 4, 128) | 0 |
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| conv2d_3 (Conv2D) | (None, 2, 2, 128) | 147,584 |
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| flatten (Flatten) | (None, 512) | 0 |
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| dense (Dense) | (None, 96) | 49,248 |
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| dropout_3 (Dropout) | (None, 96) | 0 |
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| dense_1 (Dense) | (None, 96) | 9,312 |
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| dropout_4 (Dropout) | (None, 96) | 0 |
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| dense_2 (Dense) | (None, 64) | 6,208 |
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| dense_3 (Dense) | (None, 7) | 455 |
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Total params: 306,247 (1.17 MB)
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Trainable params: 305,863 (1.17 MB)
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Non-trainable params: 384 (1.50 KB)
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## Training details
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- Dataset: https://www.kaggle.com/datasets/manishshah120/facial-expression-recog-image-ver-of-fercdataset
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| Name | Value |
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|------|-------|
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| Input shape | 48x48 (48, 48, 1) |
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| Optimizer | Adam |
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| Loss | Crossentropy |
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| Max epochs | 200 |
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| Early stopping monitor | val_loss |
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| Early stopping patience | 12 |
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## Model performance
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- Training Accuracy: 0.5758 (Epoch #84)
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- Training Loss: 1.1272 (Epoch #84)
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- Validation Accuracy: 0.5823 (Epoch #84)
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- Validation Loss: 1.1285 (Epoch #84)
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### Classification report
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```
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precision recall f1-score support
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0 0.52 0.40 0.45 491
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1 0.00 0.00 0.00 55
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2 0.43 0.17 0.25 528
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3 0.83 0.84 0.83 879
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4 0.51 0.67 0.58 626
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5 0.39 0.58 0.47 594
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6 0.73 0.72 0.73 416
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accuracy 0.58 3589
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macro avg 0.49 0.48 0.47 3589
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weighted avg 0.58 0.58 0.57 3589
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```
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## Notebook
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Training notebook: https://www.kaggle.com/code/harkishankhuva/facial-expression-classification
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