RGB-X models are advanced machine learning models in computer vision that extend traditional RGB (Red, Green, Blue) data by incorporating additional channels such as depth, infrared, or surface normals. These models have found applications across various industries and use cases, including object tracking across frames and surveying difficult terrain. Recent advancements in RGB-X model development have led to significant improvements in performance and capabilities, with challenges and considerations related to data complexity, model interpretability, and ethics and privacy. Integrating RGB-X models with vector databases like Milvus enhances their applications by enabling efficient storage, indexing, and retrieval of the rich embeddings produced by these models.