Portfolio Detail

Implemented a fruit classification system utilizing transfer learning with several pre-trained models, including VGG16, VGG19, InceptionV3, Xception, and ResNet50. This project aimed to leverage advanced deep learning architectures to classify various types of fruits accurately. By employing these state-of-the-art models, we achieved enhanced classification performance through transfer learning, which involves fine-tuning pre-trained networks on a new fruit dataset.

The use of multiple models allowed for comparative analysis and optimization, resulting in a robust classification system with improved accuracy and efficiency. This project highlights the effectiveness of transfer learning in adapting powerful neural networks to specialized tasks, such as fruit classification.