An image classification method based on few-shot learning

Image classification technology is at the heart of many complex computer vision applications, including object tracking, video categorization, and action identification. In the area of picture classification, data-driven supervised learning has had considerable success, but these model's capacity to generalize is highly reliant on a huge amount of labeled data. Not all job contexts, though, may quickly acquire a sizable number of labeled dataset samples. This article builds a deep learning approach appropriate for few-sample image classification tasks utilizing Meta-Learning in order to address the issue of image classification tasks in scenarios with few samples. Finally, to confirm the success of the approach, comparative experiments are run on the handwritten letter data set from Omniglot.