Machine learning teams are transitioning their model storage from solutions like GitHub to other platforms, as demonstrated in this post which introduces a custom panel for debugging object detection models. Utilizing the Penn-Fudan Pedestrian Detection dataset and a Faster-RCNN model with a Resnet50 backbone pre-trained on the MS COCO dataset, the post outlines the process of downloading and preparing the dataset, defining preprocessing operations, and making predictions with a Torch model. Predictions are logged to Comet, where they can be visualized using a Bounding Box panel that requires predictions to be formatted into a specific JSON structure. The panel allows users to filter detected objects by label and confidence scores, and the post emphasizes the flexibility of Comet's custom panels, encouraging users to explore and create personalized visualizations using the provided tooling and documentation.