Hi @Vishal_Virmani,
Welcome to the community.
I see at the end of your colab notebook that you try to print the output of the model which is an array of [1,10,4]. So why are you doing that as this is a classification problem as you mentioned? Or this is an object detection problem?
From the previous cells I see that with the model you do:
input_tensor = tf.convert_to_tensor(
      np.expand_dims(image_np, 0), dtype=tf.float32)
  detections, predictions_dict, shapes = detect_fn(input_tensor)

and then you do:
label_id_offset = 1
  image_np_with_detections = image_np.copy()

  viz_utils.visualize_boxes_and_labels_on_image_array(
        image_np_with_detections,
        detections['detection_boxes'][0].numpy(),
        (detections['detection_classes'][0].numpy() + label_id_offset).astype(int),
        detections['detection_scores'][0].numpy(),
        category_index,
        use_normalized_coordinates=True,
        max_boxes_to_draw=200,
        min_score_thresh=.5,
        agnostic_mode=False,
  )

  plt.figure(figsize=(12,16))
  plt.imshow(image_np_with_detections)
  plt.show()

so you use the model output, you post process it and then visualize the boxes on the image.
You can think the output of the Interpreter as the output of the saved-model. So you have to do the same procedure there also eg. preprocess the image, make the predictions with the Interpreter, post process and create the boxes on the image.
If indeed this is an Object detection project check a good example here:
  
      

      github.com
  

  
    

tensorflow/examples 1

  master/lite/examples/object_detection/android

  TensorFlow examples. Contribute to tensorflow/examples development by creating an account on GitHub.