👕 CNN with MobileNetV2 & Keras Callbacks on Fashion MNIST

This project demonstrates how to use MobileNetV2 as a feature extractor for the Fashion MNIST dataset, combining transfer learning with a custom classification head.
It also showcases the use of Keras callbacks — including EarlyStopping, ModelCheckpoint, and TensorBoard — to optimise training and track performance.

The model’s best hyperparameters (dense units, dropout, learning rate, optimiser) were sourced from prior hyperparameter tuning using Random Search, which can be found here: cnn-keras-tuning-fashion-mnist.

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🚀 Features

• Uses MobileNetV2 pretrained on ImageNet for feature extraction
• Adds a custom dense layer & dropout for Fashion MNIST classification
• Implements key Keras callbacks:
  • EarlyStopping → stops training when validation loss stops improving
  • ModelCheckpoint → saves the best-performing model automatically
  • TensorBoard → visualises training curves, histograms, and more
• Evaluates model performance across multiple independent runs to assess stability

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📂 Project Structure

cnn-callbacks-fashion-mnist/
│
├── callbacks_fashion_mnist.py   # Main training script
├── saved_models/                # Best model saved as .keras
├── logs/                        # TensorBoard logs
├── requirements.txt
├── .gitignore
└── README.md

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🛠 Installation & Setup

1️⃣ Clone the repo

git clone https://github.com/adabyt/cnn-callbacks-fashion-mnist.git
cd cnn-callbacks-fashion-mnist

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the script

python callbacks_fashion_mnist.py

4️⃣ Launch TensorBoard (optional)

tensorboard --logdir logs/fit

Then open http://localhost:6006 to visualise training.

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📊 Model Summary

The final model structure:

• MobileNetV2 (frozen) → feature extraction
• Global Average Pooling
• Dense (480 units, ReLU)
• Dropout (0.3)
• Dense (10 units, Softmax)

Total trainable parameters: ~619k (MobileNetV2 frozen)

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📈 Results & Observations

🔍 Performance Across Runs

The model was trained multiple times to evaluate run-to-run consistency:

Run	Test Accuracy	Test Loss	Epochs Used
1	0.9079	0.2543	16
2	0.9076	0.2590	17
3	0.9056	0.2559	17
4	0.9080	0.2534	17
5	0.9065	0.2555	17
6	0.9052	0.2583	14

Overall Performance:
`0.9068 ± 0.0011` after 6 runs.

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📌 Why do results vary per run?

1. Shuffle order – Different batch orders change weight updates.
2. Weight initialisation – Initial weights differ unless a seed is fixed.
3. Dropout randomness – Different neurons drop each epoch.
4. Adam optimiser’s internal state – Tracks moving averages of gradients, which evolve differently per run.
5. GPU non-determinism (less likely for smaller models) – Minor floating-point differences can accumulate.

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📉 Signs of Overfitting

The logs suggest mild overfitting: training accuracy improved faster than validation accuracy.

Ways to mitigate overfitting:

• Add more data
• Use data augmentation
• Apply regularization (L1/L2)
• Increase dropout
• Rely on early stopping
• Add batch normalisation
• Simplify model architecture
• Tune hyperparameters further

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🏁 Conclusion

This project shows how:

• Transfer learning with MobileNetV2 can effectively classify Fashion MNIST with minimal training time.
• Callbacks like EarlyStopping and ModelCheckpoint prevent overtraining and save the best model automatically.
• TensorBoard provides invaluable visualisation for debugging and performance tracking.

Final test accuracy: ~90.7%.