

Evaluate
Object detection models are commonly evaluated with a set of COCO-style metrics.
You can use one of the existing metrics implementations, but here you'll use the one from torchvision to evaluate the final
model that you pushed to the Hub.
To use the torchvision evaluator, you'll need to prepare a ground truth COCO dataset. The API to build a COCO dataset
requires the data to be stored in a certain format, so you'll need to save images and annotations to disk first. Just like
when you prepared your data for training, the annotations from the cppe5["test"] need to be formatted. However, images
should stay as they are.
The evaluation step requires a bit of work, but it can be split in three major steps.
First, prepare the cppe5["test"] set: format the annotations and save the data to disk.