  Obtaining gold standard annotated data for object detection is often costly,
involving human-level effort. Semi-supervised object detection algorithms solve
the problem with a small amount of gold-standard labels and a large unlabelled
dataset used to generate silver-standard labels. But training on the silver
standard labels does not produce good results, because they are
machine-generated annotations. In this work, we design a modified loss function
to train on large silver standard annotated sets generated by a weak annotator.
We include a confidence metric associated with the annotation as an additional
term in the loss function, signifying the quality of the annotation. We test
the effectiveness of our approach on various test sets and use numerous
variations to compare the results with some of the current approaches to object
detection. In comparison with the baseline where no confidence metric is used,
we achieved a 4% gain in mAP with 25% labeled data and 10% gain in mAP with 50%
labeled data by using the proposed confidence metric.
