tensorflow/models · error · ValueError

maximum change in aspect ratio must be between 0 and 0.5

Error message

maximum change in aspect ratio must be between 0 and 0.5

What it means

Error "maximum change in aspect ratio must be between 0 and 0.5" thrown in tensorflow/models.

Source

Thrown at official/projects/yolo/ops/preprocessing_ops.py:384

      scaled dimension / original dimension.
    cast([original_width, original_height, width, height, ptop, pleft, pbottom,
      pright], tf.float32): a `Tensor` containing the information of the image
        andthe applied preprocessing.
  """

  def intersection(a, b):
    """Finds the intersection between 2 crops."""
    minx = tf.maximum(a[0], b[0])
    miny = tf.maximum(a[1], b[1])
    maxx = tf.minimum(a[2], b[2])
    maxy = tf.minimum(a[3], b[3])
    return tf.convert_to_tensor([minx, miny, maxx, maxy])

  def cast(values, dtype):
    return [tf.cast(value, dtype) for value in values]

  if jitter > 0.5 or jitter < 0:
    raise ValueError('maximum change in aspect ratio must be between 0 and 0.5')

  with tf.name_scope('resize_and_jitter_image'):
    # Cast all parameters to a usable float data type.
    jitter = tf.cast(jitter, tf.float32)
    original_dtype, original_dims = image.dtype, tf.shape(image)[:2]

    # original width, original height, desigered width, desired height
    original_width, original_height, width, height = cast(
        [original_dims[1], original_dims[0], desired_size[1], desired_size[0]],
        tf.float32)

    # Compute the random delta width and height etc. and randomize the
    # location of the corner points.
    jitter_width = original_width * jitter
    jitter_height = original_height * jitter
    pleft = random_uniform_strong(
        -jitter_width, jitter_width, jitter_width.dtype, seed=seed)
    pright = random_uniform_strong(

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/yolo/ops/preprocessing_ops.py:384 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/0f47ef3d6377630d. Report an issue: GitHub.