title: when to stop training object detection tensorflow
body: <p>I am training faster rcnn model on fruit dataset using a pretrained model provided in google api(faster_rcnn_inception_resnet_v2_atrous_coco). </p>
  
  <p>I made few changes to the default configuration. (number of classes : 12 fine_tune_checkpoint: path to the pretrained checkpoint model and from_detection_checkpoint: true). Total number of annotated images I have is around 12000.</p>
  
  <p>After training for 9000 steps, the results I got have an accuracy percent below 1, though I was expecting it to be atleast 50% (In evaluation nothing is getting detected as accuracy is almost 0). The loss fluctuates in between 0 and 4.<a href="https://i.stack.imgur.com/3jCNf.png" rel="noreferrer"><img src="https://i.stack.imgur.com/3jCNf.png" alt="enter image description here"></a></p>
  
  <p>What should be the number of steps I should train it for. I read an article which says to run around 800k steps but its the number of step when you train from scratch? </p>
  
  <p>FC layers of the model are changed because of the different number of the classes but it should not effect those classes which are already present in the pre-trained model like 'apple'?  </p>
  
  <p>Any help would be much appreciated! </p>