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CNN Image Classification

Built and trained a custom deep CNN with strong data augmentation and tracked performance over long training. The goal was to push accuracy through iteration and disciplined evaluation.

Python PyTorch CNN CIFAR-10

GitHub Notebook

Project Summary

Goal

Train a deep CNN and improve accuracy using augmentation + tuning

Dataset

CIFAR-10 (32×32 RGB, 10 classes)

Training (final run)

400 epochs • long training with tracked metrics

Best accuracy

~96.6% (training accuracy)

Model Architecture

Deep CNN with stacked convolution blocks
Conv blocks use BatchNorm + LeakyReLU + MaxPool
Dropout used to reduce overfitting
Final classifier head outputs 10 classes

Training Setup

Loss: CrossEntropyLoss
Optimizer: SGD + momentum (stable training for CNNs)
Regularization: weight decay + dropout
Scheduler: learning-rate decay (for late-stage refinement)

Data Augmentation

Used to improve generalization and reduce overfitting.

RandomHorizontalFlip
RandomRotation
RandomResizedCrop
Normalize

Results

Peak training accuracy: ~96.6%
Training remained stable across long runs (hundreds of epochs)
Saved curves + logs to track loss/accuracy over time

What I Learned

Why augmentation boosts robustness
How BatchNorm stabilizes deeper networks
How LR scheduling affects late-epoch improvements
How to debug training using curves + logs

Next Improvements

Report test accuracy + per-class accuracy
Add confusion matrix and error analysis
Try stronger architectures (ResNet/EfficientNet)
Make evaluation transforms deterministic (no random augments in test)

Training Log (evidence)

Screenshot from late-stage training showing high accuracy around epoch 250+.

Curves