OctaCycleCNN: High-Performance Framework for 91% CIFAR-10 Accuracy

A deep learning framework demonstrating CNN training optimization on CIFAR-10, achieving 91% accuracy through modern training techniques and architectural improvements.

🎯 Final Examination Work

The complete examination project demonstrating CNN training techniques and achieving 91% accuracy on CIFAR-10 can be found at:

examinationsarbete/gustaf_boden_alpha_final.ipynb

🏆 Examination Results

The thesis project demonstrates:

• 91% CIFAR-10 Accuracy: Achieved in 20 epochs using optimized training methods 1
• OneCycleLR Optimization: Learning rate scheduling eliminating "hockey stick" training curves 2
• Efficient Training: 1.5 minute training time on RTX 2070 SUPER hardware 1
• Comprehensive Analysis: Error analysis, confusion matrices, and performance evaluation

🔬 Technical Contributions

• Custom CNN Architecture: EightLayerConvNet with optimized design for CIFAR-10
• Training Framework: Custom ModelTrainer with FP16, early stopping, and batch-level scheduling 3
• Hyperparameter Optimization: Systematic exploration using WandB integration
• Performance Optimization: Orthogonal initialization, reflection padding, and scheduler improvements

📊 Project Structure

• examinationsarbete/: Complete examination thesis and analysis
• deep_learning_tools/: Custom training infrastructure developed for the project
• Supporting materials and experimental notebooks

📈 Key Achievement

91% CIFAR-10 accuracy in 20 epochs - implementing modern deep learning optimization techniques and efficient training methodologies.