title: Understanding input and output of tflite model
body: <p>I am brand new to Tensorflow and trying to incorporate it into my project. I am using a raspberry pi4 with a camera to detect change from my bird feeder, take a picture, and then identify that bird with tensorflow. I am using the <a href="https://tfhub.dev/google/aiy/vision/classifier/birds_V1/1" rel="nofollow noreferrer">Birds_V1</a> to accomplish this.</p>
  <p>I am curious as to how to interpret the input and output. According to the overview the input is:</p>
  <blockquote>
  <p>expected to be 3-channel RGB color images of size 224 x 224, scaled to [0, 1].</p>
  </blockquote>
  <p>I am confused by what is meant by scaled to [0,1]
  Furthermore, the output</p>
  <blockquote>
  <p>image_classifier: A probability vector of dimension 965, corresponding to a background class and 964 bird species in the labelmap.</p>
  </blockquote>
  <p>I am completely lost by what is meant here.</p>
  <p>Lastly, I ran interpreter.get_input_details() and interpreter.get_output_details()  to see what it would output to me.</p>
  <p>I got this printed back, with the top being the input and bottom the output:</p>
  <pre><code>[{'name': 'module/hub_input/images_uint8', 'index': 170, 'shape': array([  1, 224, 224,   3]), 'dtype': &lt;class 'numpy.uint8'&gt;, 'quantization': (0.0078125, 128), 'quantization_parameters': {'scales': array([0.0078125], dtype=float32), 'zero_points': array([128]), 'quantized_dimension': 0}}]
  
  [{'name': 'module/prediction', 'index': 171, 'shape': array([  1, 965]), 'dtype': &lt;class 'numpy.uint8'&gt;, 'quantization': (0.00390625, 0), 'quantization_parameters': {'scales': array([0.00390625], dtype=float32), 'zero_points': array([0]), 'quantized_dimension': 0}}]
  </code></pre>
  <p>I don't know how to interpret this, and was wondering if I should be going off of this instead or ignoring it if its not important.</p>
  <p>I appreciate any clarification that can be given to any part of this, and appreciate any sources that you may deem useful for solving this. I have looked for focumentation for help, but haven't gotten anywhere yet.</p>