text: Title: [DetectionDataset] - expand `from_yolo` to include support for OBB (Oriented Bounding Boxes)
  Body: ### Description
  
  In [supervision-0.18.0](https://github.com/roboflow/supervision/releases/tag/0.18.0), we added initial support for OBB; it's time to extend it to include dataset loading.
  
  Make the necessary changes in [sv.DetectionDataset.from_yolo](https://supervision.roboflow.com/0.19.0/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to enable loading OBB datasets from disk in YOLO format. [Here](https://docs.ultralytics.com/datasets/obb/) you can read more about the YOLO OBB Format. In short, each line of the `.txt` file should have the following format.
  
  ```
  class_index, x1, y1, x2, y2, x3, y3, x4, y4
  ```
  
  The [sv.OrientedBoxAnnotator](https://supervision.roboflow.com/0.19.0/annotators/#supervision.annotators.core.OrientedBoxAnnotator.annotate) expects information about oriented bounding boxes to be stored in the `xyxyxyxy` field of [`sv.Detections.data`](https://github.com/roboflow/supervision/blob/4729e20a9408fbe08d342cc4dbae835d808686a5/supervision/detection/core.py#L88). Ensure that the information loaded from the dataset is stored there.
  
  ### API
  
  Here's an example of how to use the new API. Roboflow allows for the export of segmentation datasets as OBB. Let's ensure that our support for OBB definitely works with datasets exported from Roboflow.
  
  ```python
  import random
  import roboflow
  from roboflow import Roboflow
  import supervision as sv
  
  roboflow.login()
  rf = Roboflow()
  
  project = rf.workspace("roboflow-jvuqo").project("fashion-assistant")
  version = project.version(3)
  dataset = version.download("yolov8-obb")
  
  train_ds = sv.DetectionDataset.from_yolo(
      images_directory_path=f"{dataset.location}/train/images",
      annotations_directory_path=f"{dataset.location}/train/labels",
      data_yaml_path=f"{dataset.location}/data.yaml"
  )
  
  image_name = random.choice(list(train_ds.images))
  image = train_data.images[image_name]
  detections = train_data.annotations[image_name]
  
  oriented_box_annotator = sv.OrientedBoxAnnotator()
  annotated_frame = oriented_box_annotator.annotate(
      scene=image.copy(),
      detections=detections
  )
  ```
  
  ### Additional
  
  - Note: Please share a Google Colab with minimal code to test the new feature. We know it's additional work, but it will speed up the review process. The reviewer must test each change. Setting up a local environment to do this is time-consuming. Please ensure that Google Colab can be accessed without any issues (make it public). Thank you! 🙏🏻 