IBM Machine Learning with Python - final assignment
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Updated
Mar 10, 2021 - Jupyter Notebook
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accuracy-evaluation
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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
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Updated
Dec 11, 2023 - Python
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