Adapting VGG16: Exploring Strategies for Real-World Image Classification Challenges using CIFAR-10 to CIFAR-100

The study aims to address the complexities associated with deploying VGG16 in dynamic, real-world scenarios, where environmental conditions, object scales, and backgrounds exhibit significant variability. In the pursuit of enhancing the adaptability of the Visual Geometry Group 16 (VGG16) convolutional neural network (CNN) for real-world image classification challenges, this research delves into comprehensive strategies using the CIFAR-10 and the CIFAR-100 dataset. Leveraging advanced data augmentation techniques, including rotation, shifting, shearing, zooming, and horizontal flipping, the model is subjected to more realistic challenges, simulating the intricacies encountered in diverse environments. The VGG16 model, pre-trained on ImageNet, undergoes fine-tuning to tailor its features to the CIFAR dataset. Through systematic experimentation, this research explores the impact of different architectural modifications, including an up-sampling layer and dense layers, on the model's adaptability and robustness. The training process involves a 10–15 ages preparing regimen with a carefully crafted data augmentation pipeline, facilitating the exploration of adaptive strategies for real-world scenarios. Evaluation on a test set unveils the model's accuracy in classifying CIFAR images. The training history, depicts through accuracy curves, provides insights into the model's learning trajectory over epochs. The findings contribute valuable insights into the nuanced strategies required to adapt VGG16 to real-world challenges, providing a foundation for practitioners seeking to optimize deep learning models for image classification in dynamic and varied environments.