Add `BoundingBox` feature
My proposal would look like this:

```python
from datasets import load_dataset
from datasets.features import Sequence, BboxFeature

# load images along with boxes
dataset = load_dataset("imagefolder", data_dir="/path/to/folder", split="train")

# map the boxes column to the appropriate format
dataset = dataset.map(lambda example: map_to_ymin_xmin_ymax_xmax(example))

# cast to feature
dataset = dataset.cast_column('boxes', Sequence(BboxFeature))
````
We could for instance make [BboxFeature](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/BBoxFeature) default to normalized coordinates in the ymin, xmin, ymax, xmax format. This is just one of the many different formats of bounding boxes, so we would for instance default to the format used by Tensorflow datasets.
... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them).

cc @NielsRogge @severo