title: What is the proper way of doing inference on a batch of images in Tensorflow for performance purposes
body: <p>What I am trying to accomplish is doing inference in Tensorflow with a batch of images at a time instead of a single image. I am wondering what is the most appropriate way of handling processing of multiple images to speed up inference?</p>
  
  <p>Doing inference on a single image is easily done and quite used in most tutorials, but what I have not seen yet is doing that in a batch-like style.</p>
  
  <p>Here's what I am currently using at a high level:</p>
  
  <pre><code>pl = tf.placeholder(tf.float32)
  ...
  sess.run([boxes, confs], feed_dict={pl: image})
  </code></pre>
  
  <p>I would appreciate any input on this.</p>