DreamSim is a new algorithm designed to compare images holistically by assessing images on their mid-level attributes such as object layout, position, and semantic context. Unlike standard image comparison algorithms that only compare images based on colors and textures, DreamSim evaluates an image on its overall appearance and composition, giving it a more well-rounded comparison. The algorithm uses synthetic data to train its network but can still perform well on real images. A key point is that DreamSim showed better results than existing comparison algorithms in identifying objects and overall semantic context within an image. In summary, DreamSim is a valuable image comparison tool that captures features beyond low-level colors and textures while providing a stronger foundation for visual analysis.