Deep CNN for Fashion MNIST – Stage 3 Model Tuning
This model is part of a structured project focused on building and improving deep convolutional neural networks (CNNs) for clothing item classification using the Fashion MNIST dataset.
This is Stage 3, where architectural tuning with Batch Normalisation and Dropout was introduced to improve performance and generalisation.
Architecture Summary
- Conv2D layers: 32 → 64 → 128 filters
- Batch Normalisation + ReLU after each
- MaxPooling after each block
- Dropout applied after conv blocks and dense layer
- Dense(128) → Dense(10) with softmax output
Evaluation Metrics (on Test Set)
| Metric | Value |
|---|---|
| Accuracy | 0.9012 |
| Precision | 0.9053 |
| Recall | 0.9012 |
| F1 Score | 0.8992 |
Dataset
- Fashion MNIST (28x28 grayscale images)
- 60,000 training samples
- 10,000 test samples
- 10 clothing classes
Author
Alfred Ogunbayo – MSc AI
GitHub: https://github.com/freddylags
Hugging Face: https://huggingface.co/alfred-ogunbayo
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