👁️
AIMS Coursework
CNN Architectures (CIFAR-10)
Late 2024 — From SimpleCNN to BN_AsimNet
CNN
Batch Normalization
Dropout
Data Augmentation
About
Systematic exploration of CNN architectures for CIFAR-10 image classification. Investigating the effects of model complexity, regularization (Dropout, Batch Normalization), and data augmentation on classification accuracy.
View on GitHub →Dataset: CIFAR-10
60K
Total images
10
Classes
32×32
Image size
RGB
Color
Classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck
Model Architectures
1. SimpleCNN
2 conv layers + ReLU + 2 max-pool + 3 FC layers
2. ImprovedCNN
3 conv layers with increased filter depth + reduced FC
3. AsimNet
4 conv layers with increasing filter sizes + 512 FC neurons
4. BN_AsimNet ⭐
AsimNet + Batch Normalization after each conv + Dropout (0.25)
Results
| Model | Val Accuracy | Val Loss |
|---|---|---|
| SimpleCNN | ~51% | 1.87 |
| ImprovedCNN | ~65% | 1.20 |
| AsimNet | ~72% | 0.98 |
| BN_AsimNet ⭐ | ~77% | 0.82 |
Observations:
- • SimpleCNN: Baseline with limited capacity for complex patterns
- • ImprovedCNN: Benefits from deeper architecture and more filters
- • AsimNet: Higher capacity, better generalization but prone to overfitting
- • BN_AsimNet: BatchNorm + Dropout provide best balance of performance and generalization
Key Takeaways
- ✅ Batch Normalization + Dropout together provide best results
- ✅ Deeper networks capture more complex patterns (51% → 77%)
- ✅ Data augmentation (random crop, horizontal flip) improves generalization
- ✅ Regularization is essential for larger models to prevent overfitting