使用对象检测的默认配置时，不同尺寸的图像缩放器有什么影响 api

Question

我试图使用 Tensorflow 的对象检测 API 来训练模型。我正在使用更快的 rcnn resnet101 (https://github.com/tensorflow/models/blob/master/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config) 的示例配置。
以下代码是我不太理解的配置文件的一部分：

image_resizer {
  keep_aspect_ratio_resizer {
    min_dimension: 600
    max_dimension: 1024
  }
}

我的问题是：

min_dimension 和 max_dimension 的确切含义是什么？这是否意味着输入图像的大小将调整为 600x1024 或 1024x600？
如果我有不同大小的图像，也许其中一些相对大于 600x1024（或 1024x600），could/should 我增加 min_dimension 和 max_dimension 的值？

我之所以会有这样的疑问，是因为这个post：

在这个post中，作者自己给出了问题的答案：

Then I decided to crop the input image and provide that as an input. Just to see if the results improve and it did!
It turns out that the dimensions of the input image were much larger than the 600 x 1024 that is accepted by the model. So, it was scaling down these images to 600 x 1024 which meant that the cigarette boxes were losing their details :)

它使用的配置与我使用的相同。而且我不确定我是否可以更改这些参数，如果它们是默认设置或推荐设置到这个特殊模型，faster_rcnn_resnet101。

Answer 1

经过一些测试，我想我找到了答案。如有不妥请指正

在 .config 文件中：

image_resizer {
  keep_aspect_ratio_resizer {
    min_dimension: 600
    max_dimension: 1024
  }
}

根据 'object_detection/builders/image_resizer_builder.py'

的图像缩放器设置

if image_resizer_config.WhichOneof(
    'image_resizer_oneof') == 'keep_aspect_ratio_resizer':
  keep_aspect_ratio_config = image_resizer_config.keep_aspect_ratio_resizer
  if not (keep_aspect_ratio_config.min_dimension
          <= keep_aspect_ratio_config.max_dimension):
    raise ValueError('min_dimension > max_dimension')
  return functools.partial(
      preprocessor.resize_to_range,
      min_dimension=keep_aspect_ratio_config.min_dimension,
      max_dimension=keep_aspect_ratio_config.max_dimension)

然后尝试使用'object_detection/core/preprocessor.py'

的'resize_to_range'函数

  with tf.name_scope('ResizeToRange', values=[image, min_dimension]):
    image_shape = tf.shape(image)
    orig_height = tf.to_float(image_shape[0])
    orig_width = tf.to_float(image_shape[1])
    orig_min_dim = tf.minimum(orig_height, orig_width)

    # Calculates the larger of the possible sizes
    min_dimension = tf.constant(min_dimension, dtype=tf.float32)
    large_scale_factor = min_dimension / orig_min_dim
    # Scaling orig_(height|width) by large_scale_factor will make the smaller
    # dimension equal to min_dimension, save for floating point rounding errors.
    # For reasonably-sized images, taking the nearest integer will reliably
    # eliminate this error.
    large_height = tf.to_int32(tf.round(orig_height * large_scale_factor))
    large_width = tf.to_int32(tf.round(orig_width * large_scale_factor))
    large_size = tf.stack([large_height, large_width])

    if max_dimension:
      # Calculates the smaller of the possible sizes, use that if the larger
      # is too big.
      orig_max_dim = tf.maximum(orig_height, orig_width)
      max_dimension = tf.constant(max_dimension, dtype=tf.float32)
      small_scale_factor = max_dimension / orig_max_dim
      # Scaling orig_(height|width) by small_scale_factor will make the larger
      # dimension equal to max_dimension, save for floating point rounding
      # errors. For reasonably-sized images, taking the nearest integer will
      # reliably eliminate this error.
      small_height = tf.to_int32(tf.round(orig_height * small_scale_factor))
      small_width = tf.to_int32(tf.round(orig_width * small_scale_factor))
      small_size = tf.stack([small_height, small_width])

      new_size = tf.cond(
          tf.to_float(tf.reduce_max(large_size)) > max_dimension,
          lambda: small_size, lambda: large_size)
    else:
      new_size = large_size

    new_image = tf.image.resize_images(image, new_size,
                                       align_corners=align_corners)

从上面的代码中，我们可以知道是否有一张尺寸为800*1000的图片。最终输出图片的大小为600*750。

也就是说，此图像缩放器将始终根据 'min_dimension' 和 'max_dimension' 的设置调整输入图像的大小。

使用对象检测的默认配置时，不同尺寸的图像缩放器有什么影响 api

What the impact of different dimension of image resizer when using default config of object detection api

object-detection

tensorflow