Using pre-trained inception_resnet_v2 with Tensorflow
<p>I have been trying to use the pre-trained inception_resnet_v2 model released by Google. I am using their model definition(<a href="https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py" rel="nofollow">https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py</a>) and given checkpoint(<a href="http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz" rel="nofollow">http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz</a>) to load the model in tensorflow as below [Download a extract the checkpoint file and download sample images dog.jpg and panda.jpg to test this code]-</p>

<pre><code>import tensorflow as tf
slim = tf.contrib.slim
from PIL import Image
from inception_resnet_v2 import *
import numpy as np

checkpoint_file = 'inception_resnet_v2_2016_08_30.ckpt'
sample_images = ['dog.jpg', 'panda.jpg']
#Load the model
sess = tf.Session()
arg_scope = inception_resnet_v2_arg_scope()
with slim.arg_scope(arg_scope):
  logits, end_points = inception_resnet_v2(input_tensor, is_training=False)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_file)
for image in sample_images:
  im = Image.open(image).resize((299,299))
  im = np.array(im)
  im = im.reshape(-1,299,299,3)
  predict_values, logit_values = sess.run([end_points['Predictions'], logits], feed_dict={input_tensor: im})
  print (np.max(predict_values), np.max(logit_values))
  print (np.argmax(predict_values), np.argmax(logit_values))
</code></pre>

<p>However, the results from this model code does not give the expected results (class no 918 is predicted irrespective of the input image). Can someone help me understand where I am going wrong?</p>

<p>Thanks!</p>

<p>The Inception networks expect the input image to have color channels scaled from [-1, 1]. As seen <a href="https://github.com/tensorflow/models/blob/master/slim/preprocessing/inception_preprocessing.py#L237-L275" rel="nofollow">here</a>.</p>

<p>You could either use the existing preprocessing, or in your example just scale the images yourself: <code>im = 2*(im/255.0)-1.0</code> before feeding them to the network.</p>

<p>Without scaling the input [0-255] is much larger than the network expects and the biases all work to very strongly predict category 918 (comic books).</p>

python computer-vision tensorflow deep-learning