title: Problem with classification using yolo darknet
body: <p>I have trained my custom object detector, using yolo darknet, with 3 classes : </p>
  
  <pre><code> - human (full body)
   - high visibility jacket (HVJ)
   - safety helmet
  </code></pre>
  
  <p>It work great! But I need to add a features :</p>
  
  <pre><code> - if in the frame an human wears nothing (not HVJ/helmet), it is tracked with red box
   - if in the frame an human wears an HVJ, it is tracked with yellow box 
   - if in the frame an human wears an helment, it is tracked with yellow box
   - if in the frame an human wears an helmet and an HVJ, it is tracked with green box
  </code></pre>
  
  <p>How can I implement it? Currently with my trained model cannot do it.
  I'm thinking that I could create a new dataset with the following classes and train the model again with the classes :</p>
  
  <pre><code>- human with nothing 
  - human with only HVJ
  - human with only helmet
  - human with HSJ and helmet
  </code></pre>
  
  <p>But I am not convinced that this method can work. 
  Can you give me some suggestions?
  If it can be useful to know for the testing mode I use a script in python/OpenCV.</p>