text: Title: Unexpected Negative Coordinates in YOLOv8 Annotations after Transformations
  Body: **Description:**
  I am encountering an issue while applying transformations to YOLOv8 annotations (object detection format) using albumentations. Below is a minimal reproducible example
  
  ```
  import cv2
  import albumentations as A
  
  image = cv2.imread("....")
  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  bboxes = [[0.231627, 0.014903, 0.047738, 0.029807]]  
  category_ids = [0]
  category_id_to_name = {0: 'part'}
  
  transform = A.Compose(
      [A.HorizontalFlip(p=0.5)],  # Horizontal flip transformation
      bbox_params=A.BboxParams(format='yolo', label_fields=['category_ids'])
  )
  
  transformed = transform(image=image, bboxes=bboxes, category_ids=category_ids)
  ```
  **Error:**
  When applying different transformations (even an empty one), I consistently receive the following error
  ```
  ValueError: Expected y_min for bbox [ 2.0775801e-01 -5.0012022e-07  2.5549600e-01  2.9806498e-02  0.0000000e+00] to be in the range [0.0, 1.0], got -5.00120222568512e-07.
  ```
  **Observations:**
  Input image shape  (672, 504, 3)
  The original bounding box coordinates are well within the valid range [0.0, 1.0].
  Drawing the bounding boxes on the original image using a custom function shows correct placement and dimensions.
  The error consistently produces the same invalid numbers:
  ```
  [2.0775801e-01, -5.0012022e-07, 2.5549600e-01, 2.9806498e-02, 0.0000000e+00]
  ```
  **Custom Function for Verification:**
  Below is the custom function I used to verify the correctness of the bounding boxes by visualizing them on the image:
  ```
  import cv2
  
  def draw_bboxes(image_path, annotations_path):
      image = cv2.imread(image_path)
      height, width, _ = image.shape
      with open(annotations_path, 'r') as f:
          annotations = f.readlines()
          _, x_center, y_center, bbox_width, bbox_height = map(float, annotation.strip().split())
          x_center = int(x_center * width)
          y_center = int(y_center * height)
          bbox_width = int(bbox_width * width)
          bbox_height = int(bbox_height * height)
          x1 = int(x_center - bbox_width / 2)
          y1 = int(y_center - bbox_height / 2)
          x2 = int(x_center + bbox_width / 2)
          y2 = int(y_center + bbox_height / 2)
  
          cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Green, line thickness = 2
  
      # Save the result
      cv2.imwrite('output_image.jpg', image)
  ```
  **Notes:**
  
  The bounding boxes drawn using this function align correctly with the objects in the image.
  The issue seems to be related to how albumentations processes the YOLO format during transformations.
  **Questions:**
  Where do the negative y_min values originate, given that the original annotations are valid?
  Is this a bug in the library, or is there a mistake in the way I define or pass the annotations?
  Any help in resolving this would be greatly appreciated!
  ![output_image](https://github.com/user-attachments/assets/00b175ad-1651-4517-81dd-35170696e9e0)
  