title: Object Detection API: Confidence score getting lower with increase in number of training steps
body: <p>I am trying to use the object detection API by TensorFlow to detect a particular pattern in a 3190X3190 image using <strong>faster_rcnn_inception_resnet_v2_atrous_coco</strong>. All my training images are  of size 1140X1140. The pattern itself is of width 380 pixels and height 430 pixels. The pattern is made up of basic shapes such as rectangles and circles. Every rectangle and circle is of the same dimensions in both the inference and training images. I have 4000 training images. I notice even though I am getting reasonable results at around 5k steps with a confidence score of around 98%, the maximum confidence score drops to around 5% when the number of training steps increase to 8k. I have noticed almost the same thing if I increase the size of the training image to 1290x1290 by padding a black ring around it. However, I get good results if I use training images of size 1500x1500 even if the number of steps is 25k. I have also tried with training images of size 3190x3190 (i.e. same size as the inference image) and got detections with really low confidence score.</p>
  
  <p>Is there any particular reason for this behavior? I would be interested to know as to how the size of the training image affecting the detection results. I am using version 1.4 of the object-detection API.</p>