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learning-rate-scheduler

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Yonas650 / Image-classification-VGG16

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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

python machine-learning deep-learning numpy scikit-learn pil pytorch seaborn image-classification matplotlib vgg16 data-augmentation torchvision accuracy-evaluation learning-rate-scheduler
  • Updated Dec 11, 2023
  • Python

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