title: TensorFlow Object Detection API poor results
body: <p>I have trained TensorFlow Object Detection API on my own PascalVOC dataset with 7 classes. In each class there are from 100 to 200 images. I was using <strong><em>ssd_mobilenet_v1_pets.config</em></strong> standard configurations, except number of classes and number of steps options.</p>
  
  <p>Before training I did resizing job with images so every image in my set is from 600 up to 1024px. Annotations has been done with LabelImg App.</p>
  
  <p>After train job with 100k steps has been done I tried my model using code from <a href="https://github.com/tensorflow/models/blob/master/object_detection/object_detection_tutorial.ipynb" rel="nofollow noreferrer">jupyter notebook tutorial</a> on several test images. Unfortunately results were kind of weird.</p>
  
  <p>If test image contains only one object, it works fine:</p>
  
  <p><a href="https://i.stack.imgur.com/5kHIc.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/5kHIc.png" alt="enter image description here"></a></p>
  
  <p>But if there are a lot of objects on image result is being weird or not even shows up:</p>
  
  <p><a href="https://i.stack.imgur.com/K5qAj.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/K5qAj.jpg" alt="enter image description here"></a></p>
  
  <p>You can see it detects almost the whole image for some reasons. So what can you to reach detection of multiple objects on image like on the above one.</p>
  
  <p>Will be very grateful for any help. Thanks in advance!</p>
  
  <p>Here is my label map for any case:</p>
  
  <pre><code>item {
    id: 1
    name: 'chesterfield_blue'
  }
  
  item {
    id: 2
    name: 'chesterfield_red'
  }
  
  item {
    id: 3
    name: 'lucky_strike_blue'
  }
  
  item {
    id: 4
    name: 'lucky_strike_red'
  }
  
  item {
    id: 5
    name: 'marlboro_gold'
  }
  
  item {
    id: 6
    name: 'marlboro_mentol'
  }
  
  item {
    id: 7
    name: 'marlboro_red'
  }
  </code></pre>