title: Tensorflow high false-positive rate and non-max-suppression issue
body: <p>I am training <strong>Tensorflow Object detection</strong> on Windows 10using <strong>faster_rcnn_inception_v2_coco</strong> as pretrained model. I'm on Windows 10, with tensorflow-gpu 1.6 on NVIDIA GeForce GTX 1080, CUDA 9.0 and CUDNN 7.0.</p>
  
  <p>My dataset contain only one object, "Pistol", and 3000 images (2700 train set, 300 test set). The size of the images are from ~100x200 to ~800x600.</p>
  
  <p>I trained this model for 55k iterations, where the <a href="https://imgur.com/Bj3KdaW" rel="noreferrer">mAP</a> was ~0.8 and the <a href="https://imgur.com/XQdAYmV" rel="noreferrer">TotalLoss</a> seems converged to 0.001. But however, seeing the evaluation, that there are a lot of multiple bounding boxes on the same detected object (e.g. <a href="https://imgur.com/JhQZjle" rel="noreferrer">this</a> and <a href="https://imgur.com/XR74Hzz" rel="noreferrer">this</a>), and lot of false positives (<a href="https://imgur.com/Yp8Lrc1" rel="noreferrer">house detected as a pistol</a>). For example, in <a href="https://imgur.com/qb1gOb8" rel="noreferrer">this photo</a> taked by me (blur filter was applied later), the model detect a person and a car as pistols, as well as the correct detection.</p>
  
  <p>The dataset is uploaded <a href="https://drive.google.com/open?id=1Hw1-YB0-DyouhzrH5YT_0Ylvk5v5a5JK" rel="noreferrer">here</a>, together with the tfrecords and the label map. 
  I used <a href="https://drive.google.com/open?id=1lc7U9TeowP3SL0W9wRymEJbMr5cVGQG0" rel="noreferrer">this config file</a>, where the only things that I changed are: <em>num_classes</em> to 1, the <em>fine_tune_checkpoint</em>, <em>input_path</em> and <em>label_map_path</em> for train and eval, and <em>num_examples.</em>
  Since I thought that the multiple boxes are a non-max-suppression problem, I changed the <em>score_threshold</em> (line 73) from 0 to 0.01 and the <em>iou_threshold</em> (line 74) from 1 to 0.6. With the standard values the outcome was much worse than this.</p>
  
  <p>What can I do to have a good detection? What should I change? Maybe I miss something about parameters tuning...</p>
  
  <p>Thanks</p>