<p dir="auto">Current<code class="notranslate">tf.data.dataset</code>class only support <code class="notranslate">padded_batch</code> method which would pad every component into same shape, but there are cases that some component may not be padable, e.g., in object detection input pipeline, the ground truth calsses and bounding boxes are different for different image, so they can not be padded to the same shape as images, so how to batch these data in this case?</p>
<p dir="auto">Current TF dataset class only support fixed size padding for batching different sized input elements, however there are cases like in object detection, we may need to batch images according to their aspect ratios, i.e., push images with similar aspect ratio into one batch and pad them with minimal extra paddings, is there any existing approach that does not use feed_dict approach but directly utilize TF operators and read TF record files for accomplish this ? Thanks!</p>