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Leveraging pre-trained models on large datasets for fine-tuning smaller, domain-specific image classification tasks.
This repository demonstrates the use of transfer learning for image classification tasks. Transfer learning allows models to leverage pre-trained knowledge from large datasets (such as ImageNet) to improve classification performance on smaller, domain-specific datasets. The project provides examples of how to use popular CNN architectures like VGG16, ResNet, and InceptionV3 with pre-trained weights for transfer learning, as well as techniques for fine-tuning the models to achieve better performance.
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