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license: mit
tags:
- image-classification
- cnn
- tensorflow
- keras
- fashion-mnist
datasets:
- fashion_mnist
metrics:
- accuracy
- precision
- recall
- f1
---
# 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 |