title: FATAL Flags parsing error:Unknown command line flag 'logtostderr'
body: <p><strong>System information</strong></p>
  
  <ol>
  <li>OS Platform and Distribution: Windows 10 64 Bit </li>
  <li>TensorFlow installed from (source or binary): binary</li>
  <li>TensorFlow version:1.5.0-dev20171115'</li>
  <li>Python version 3.5.2(v3.5.2:4def2a2901a5, Jun 25 2016, 22:18:55)</li>
  <li>GPU: nVidia GeForce 755M 2GB</li>
  <li>CPU: Intel x64-64 Intel Core i5-4200M CPU @ 2.50Ghz, 8GB memory</li>
  </ol>
  
  <p><strong>Describe the problem:</strong></p>
  
  <p>Initially, the problem I had was is described here: <a href="https://github.com/tensorflow/models/issues/2653" rel="nofollow noreferrer">https://github.com/tensorflow/models/issues/2653</a></p>
  
  <p>The solution given in that is to try and install tf nightly. I did that as shown :
  <a href="https://i.stack.imgur.com/kkI3E.jpg" rel="nofollow noreferrer">tfnightly</a></p>
  
  <p>However, now when I try to run the following command:</p>
  
  <pre><code>C:\Users\kannan\Documents\MDLearning\models\research\object_detection&gt;train.py --logtostderr --train_dir=training/ --
  pipeline_config_path=training/ssd_mobilenet_v1_pets.config
  </code></pre>
  
  <p><strong>I get the following Error:</strong></p>
  
  <blockquote>
    <p>FATAL Flags parsing error: Unknown command line flag 'logtostderr'
    Pass --helpshort or --helpfull to see help on flags.</p>
  </blockquote>
  
  <p>Any idea how to solve this?</p>
  
  <p><strong>EDIT:</strong>
  I omitted --logtostderr and tried the same command. I probably ended up with a memory allocation error from the looks of it as seen here in the PDF attached below. I know it is really long. To make it easy, I have highlighted the important errors: Page 1, 2, 106, 107, 111 and 115.</p>
  
  <p><a href="https://drive.google.com/file/d/1OZBDClkK7dGtvTf_ckkb7GBqW4zUHC-o/view?usp=sharing" rel="nofollow noreferrer">PDF-Memory Error</a></p>
  
  <p>Any idea how to get around this? I am using a batch size of 1 and it possibly can't go any lower. I have used around 187 images with two classes labelled in each image. All my images are 960 by 720 pixels each. My GPU compute capability is 3.0</p>