Introduction

If you're training models for object detection, you can interactively visualize bounding boxes in Weights & Biases. This short demo focuses on driving scenes, testing a YoloV3 net pretrained on MSCOCO on images from the Berkeley Deep Drive 100K dataset. The API for logging bounding boxes is flexible and intuitive. Below, I explain the interaction controls for this tool and a few ways you might use it to analyze your models.

This approach can help in object detection on many other kinds of images, from microscope slides to x-rays to satellite and beyond. You can read more about understanding driving scenes in this report and more about the Lyft's self-driving car dataset in this report

High-level view: Many examples on validation data

Section 3