title: How to run eval.py job for tensorflow object detection models
body: <p>I have trained an object detector using tensorflow's object detection API on Google Colab. After researching on the internet for most of the day, I haven't been able to find a tutorial about how to run an evaluation for my model, so I can get metrics like mAP. </p>
  
  <p>I figured out that I have to use the eval.py from the models/research/object_detection folder, but I'm not sure which parameters should I pass to the script. </p>
  
  <p>Shortly, what I've done so far is, generated the labels for the test and train images and stored them under the object_detection/images folder. I have also generated the train.record and test.record files, and I have written the labelmap.pbtxt file. I am using the faster_rcnn_inception_v2_coco model from the tensorflow model zoo, so I have configured the faster_rcnn_inception_v2_coco.config file, and stored it in the object_detection/training folder. 
  The training process ran just fine and all the checkpoints are stored also in the object_detection/training folder. </p>
  
  <p>Now that I have to evaluate the model, I ran the eval.py script like this:</p>
  
  <pre><code>!python eval.py --logtostderr --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config --checkpoint_dir=training/ --eval_dir=eval/
  </code></pre>
  
  <p>Is this okay? Because this started running fine, but when I opened tensorboard there were only two tabs, namely images and graph, but no scalars. Also, I ran tensorboard with logdir=eval.</p>
  
  <p>I am new to tensorflow, so any kind of help is welcome. 
  Thank you.</p>