<p>Solution is to not use negative hard mining, but instead use focal loss. This is in line with the Focal Loss / RetinaNet paper : <a href="https://arxiv.org/pdf/1708.02002v2.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1708.02002v2.pdf</a></p>
In object detection with SSD Mobilenetv2 + FPN(TF2 Object detection API), what are the possible reasons for hard example mining to not work well?
<p>I am using the Tensorflow Object Detection API to build a detection model. Hard example mining seemed to work really well with <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config" rel="nofollow noreferrer">SSD+Mobilenetv2 model</a>(used with the TF1 version of the API). However with similar settings in the TF2 version with FPN <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/configs/tf2/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.config" rel="nofollow noreferrer">SSD+Mobilenetv2+FPN model</a>, I achieve similar metrics for mAP on relevant category but see a lot more false positives in evaluation even after adding hard example mining. What could be the possible reasons for that, any other ways to reduce false positives?</p>