Comet has introduced a new feature called the Image Panel, designed to enhance the visualization of model performance for machine learning teams, particularly in computer vision tasks like segmentation, reconstruction, and object detection. This tool allows users to log and compare visualizations of model predictions across multiple experiments, providing a more comprehensive understanding than traditional metrics like loss and mean Average Precision (mAP). It is especially beneficial for autoencoder models, as it lets engineers visualize and compare reconstructed images side-by-side with their originals, facilitating an analysis of how different architectural choices and hyperparameters impact model performance. The Image Panel also enables users to track the progression of model predictions throughout the training process, improving insight into model development over time. This feature is easily accessible by selecting the New Panel option in Comet's interface and is available for exploration in a public project.