Principle analysis of five prominent large image classification models

Deep learning models, based on transformer architectures, have opened up new possibilities for computer vision missions such as object detection, image generation, and image classification. By encoding global context information from an image and capturing long-range dependencies these models achieve state-of-the-art performance in many benchmark datasets. This article focuses on five prominent image classification methods based on deep learning: Contrastive Captioner (Coca), Pathways Language and Image (PaLI) model, EvoLved Sign Momentum (Lion), A Large-scale Image and Noisy-text embedding (ALIGN), and ONE-PEACE. Each of these methods is briefly introduced and the general principles behind them are explained. Additionally, their practical performance in various applications is also reviewed. Most of these methods involve integrating existing datasets to train the model and focusing on the characteristics of the dataset to improve classification accuracy. This work also analyses the advantages and disadvantages of each method, as well as the functions implemented by their individual designs.