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learning-rate-scheduler

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This project modifies the classic VGG16 architecture to classify images into four distinct categories with high accuracy. It incorporates data augmentation, dynamic learning rate adjustments, and comprehensive performance evaluation using accuracy metrics and confusion matrices. Built with PyTorch and supported by a suite of powerful libraries

  • Updated Dec 11, 2023
  • Python

🖼️A PyTorch-based Convolutional Neural Network (CNN) built from scratch to classify CIFAR-10 images. Achieves 82.93% test accuracy using Batch Normalization, Dropout, Data Augmentation, and a ReduceLROnPlateau scheduler. Includes detailed evaluation curves, confusion matrices, and per-class analyses.

  • Updated Jul 24, 2026
  • Jupyter Notebook

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