

To visualize the bounding boxes with associated labels, you can get the labels from the dataset's metadata, specifically
the category field.
You'll also want to create dictionaries that map a label id to a label class (id2label) and the other way around (label2id).
You can use them later when setting up the model. Including these maps will make your model reusable by others if you share
it on the Hugging Face Hub. Please note that, the part of above code that draws the bounding boxes assume that it is in XYWH (x,y co-ordinates and width and height of the box) format. It might not work for other formats like (x1, y1, x2, y2).  
As a final step of getting familiar with the data, explore it for potential issues. One common problem with datasets for
object detection is bounding boxes that "stretch" beyond the edge of the image. Such "runaway" bounding boxes can raise
errors during training and should be addressed at this stage. There are a few examples with this issue in this dataset.
To keep things simple in this guide, we remove these images from the data.