CNN for Image Classification (CIFAR‑10)
Convolutional neural network implementation for CIFAR‑10 with training pipeline, evaluation, and experiment‑ready structure.
Overview
A practical computer vision project implementing a CNN for CIFAR‑10, with a clean training/evaluation workflow designed to be rerun and improved.
Problem
Image classification benchmarks are easy to start and hard to do well. The challenge is structuring the pipeline to support iteration: model changes, augmentation, and repeatable evaluation.
Approach
Implemented a CNN architecture with a disciplined training loop, validation, and evaluation reporting. Organized code for experimentation: clear configuration points for architecture and training settings. Documented results and next‑step improvements to show maturity beyond a single run.
Impact
Signals applied deep‑learning competence: not just building a model, but building a workflow you can iterate on like a real engineer.