pip install -U albumentations opencv-python

>>> from datasets import load_dataset

>>> ds = load_dataset("rishitdagli/cppe-5")
>>> example = ds['train'][0]
>>> example
{'height': 663,
 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x7FC3DC756250>,
 'image_id': 15,
 'objects': {'area': [3796, 1596, 152768, 81002],
  'bbox': [[302.0, 109.0, 73.0, 52.0],
   [810.0, 100.0, 57.0, 28.0],
   [160.0, 31.0, 248.0, 616.0],
   [741.0, 68.0, 202.0, 401.0]],
  'category': [4, 4, 0, 0],
  'id': [114, 115, 116, 117]},
 'width': 943}

>>> import torch
>>> from torchvision.ops import box_convert
>>> from torchvision.utils import draw_bounding_boxes
>>> from torchvision.transforms.functional import pil_to_tensor, to_pil_image

>>> categories = ds['train'].features['objects'].feature['category']

>>> boxes_xywh = torch.tensor(example['objects']['bbox'])
>>> boxes_xyxy = box_convert(boxes_xywh, 'xywh', 'xyxy')
>>> labels = [categories.int2str(x) for x in example['objects']['category']]
>>> to_pil_image(
...     draw_bounding_boxes(
...         pil_to_tensor(example['image']),
...         boxes_xyxy,
...         colors="red",
...         labels=labels,
...     )
... )

>>> import albumentations
>>> import numpy as np

>>> transform = albumentations.Compose([
...     albumentations.Resize(480, 480),
...     albumentations.HorizontalFlip(p=1.0),
...     albumentations.RandomBrightnessContrast(p=1.0),
... ], bbox_params=albumentations.BboxParams(format='coco',  label_fields=['category']))

>>> image = np.array(example['image'])
>>> out = transform(
...     image=image,
...     bboxes=example['objects']['bbox'],
...     category=example['objects']['category'],
... )

>>> image = torch.tensor(out['image']).permute(2, 0, 1)
>>> boxes_xywh = torch.stack([torch.tensor(x) for x in out['bboxes']])
>>> boxes_xyxy = box_convert(boxes_xywh, 'xywh', 'xyxy')
>>> labels = [categories.int2str(x) for x in out['category']]
>>> to_pil_image(
...     draw_bounding_boxes(
...         image,
...         boxes_xyxy,
...         colors='red',
...         labels=labels
...     )
... )

>>> def transforms(examples):
...     images, bboxes, categories = [], [], []
...     for image, objects in zip(examples['image'], examples['objects']):
...         image = np.array(image.convert("RGB"))
...         out = transform(
...             image=image,
...             bboxes=objects['bbox'],
...             category=objects['category']
...         )
...         images.append(torch.tensor(out['image']).permute(2, 0, 1))
...         bboxes.append(torch.tensor(out['bboxes']))
...         categories.append(out['category'])
...     return {'image': images, 'bbox': bboxes, 'category': categories}

>>> ds['train'].set_transform(transforms)

>>> example = ds['train'][10]
>>> to_pil_image(
...     draw_bounding_boxes(
...         example['image'],
...         box_convert(example['bbox'], 'xywh', 'xyxy'),
...         colors='red',
...         labels=[categories.int2str(x) for x in example['category']]
...     )
... )