A company needs to classify images of objects using features specific to their dataset. Which option requires the least development work while meeting this need?

Fine-tuning a pre-trained deep learning model is the most efficient approach. Deep learning models, especially those pre-trained on large datasets like ImageNet, have already learned powerful, generalizable features for image recognition. Fine-tuning involves adapting these learned features to a specific dataset with minimal additional training, requiring significantly less data and computational resources than training from scratch. Traditional machine learning algorithms with custom-extracted features or SVMs with manually engineered features require extensive domain expertise and iterative feature engineering, which is time-consuming and often less effective than deep learning for complex image tasks. Building a Generative Adversarial Network (GAN) is for generating new data, not for classification, and is a complex task itself, making it unsuitable for this requirement.