Image Classification using AlexNet on CIFAR-10

A deep-learning CNN that classifies 60,000 images across 10 categories.

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Domain
Deep Learning
Year
2025
Type
Research report — internal engineering work

This is internal engineering and R&D work, not a client project. It is published to show technical depth — the modelling, evaluation, and tooling behind the AI automation we build for clients. No client is associated with it.

This project implements a convolutional neural network based on the AlexNet architecture to classify images from the CIFAR-10 dataset of 60,000 colour images across 10 categories. The aim was a high-performance CNN that balances generalization and training efficiency.

Tools & methods

  • Python
  • PyTorch
  • Matplotlib & scikit-learn
  • CUDA GPU
  • CIFAR-10 dataset
Full technical report

Objectives

  • Implement an AlexNet-style model using PyTorch.
  • Achieve over 80% accuracy on the CIFAR-10 dataset.
  • Apply data augmentation and regularization techniques.

Process

  • Input CIFAR-10 images → data augmentation (random crop, flip, colour jitter).
  • AlexNet CNN: 3 convolutional blocks (ReLU, BatchNorm, MaxPooling) → fully-connected 1024 → 512 → 10.
  • Training: CrossEntropyLoss, Adam (LR 0.001, weight decay 1e-4), StepLR scheduler, 50 epochs, batch size 128.
  • Evaluation via accuracy and a confusion matrix.

Results

  • Final training accuracy ~90%, test accuracy 84.62%.
  • Overfitting reduced via augmentation and dropout.
  • Confusion matrix showed balanced class-wise accuracy.

What we took from it

  • Data augmentation improves generalization significantly.
  • Classic architectures like AlexNet still perform well.
  • Visualization tools are vital for model diagnostics.

From the report

  • Confusion matrix — CIFAR-10 classification with AlexNet
    Confusion matrix — CIFAR-10 classification with AlexNet

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Engineering & AI R&D

This is internal and academic engineering work. Each study includes its full technical report.

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